Rabies Genetic Diversity and Reservoir Identification in Terrestrial Carnivores

Home , Canina (subtribe), Rabies transmission

Throughout Ethiopia

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Laura Elyse Binkley

Graduate Program in Comparative and Veterinary Medicine

The Ohio State University

2019

Dissertation Committee:

Wondwossen A. Gebreyes, Advisor

Jeanette O’Quin, Co-advisor Laura Pomeroy

Andrés Velasco-Villa

Robert J. Gates

Michael S. Bisesi

Copyrighted by

Laura Elyse Binkley

2019

Abstract

Ethiopia has long been among the most rabies-affected countries in the world with an annual incidence rate of 1.6/100,000 population. Domestic dogs serve as the principal reservoir for rabies transmission however, little information exists regarding the genetic diversity of RABVs circulating in dogs or the existence of cycles maintained by other mammalian species. Identifying all reservoirs of rabies plays a crucial role in effective disease control. Objectives include 1.) Investigation of the genetic diversity of rabies circulating in wild and domestic species throughout Ethiopia; 2.) Identification of intraspecies and interspecies contact rates at communal foraging sites; 3.) Application of contact rate estimates to mathematical expressions that will help determine maintenance potential.

This work comprises the study of 230 partial and complete N-gene sequences obtained from both wild and domestic species collected throughout different regions of

Ethiopia during the period 2010-2017. Camera traps were used to examine contact rates within and between terrestrial carnivore species at communal foraging sites. These contact rates were then applied to expressions of the basic reproductive number to determine the rabies maintenance potential of populations.

Results identified the existence of a major dog rabies epizootic throughout

Ethiopia involving a homogeneous RABV variant that has been spreading from an ii epicenter in the Oromia region. Additionally, a 3.3% divergent RABV variant circulating in side-stripped jackals was identified. Intraspecies contact rates and calculations of maintenance potential were highest in domestic dogs followed by spotted hyenas and domestic cats, respectively. Interspecies contact rates and calculations of maintenance potential were highest between domestic cats and spotted hyenas. This snapshot of rabies dynamics in Ethiopia provides important baseline data for prevention and control efforts and serves as the first steps in identifying wildlife reservoir hosts for rabies transmission throughout the country.

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Acknowledgments

The Acknowledgments page is optional. This page includes a brief, sincere, professional acknowledgment of the assistance received from individuals, advisor, faculty, and institution.

Vita

2005...... Bishop Watterson High School

2009...... B.A. Zoology, Ohio Wesleyan University

2014...... M.P.H. Environmental Public Health,

Veterinary Public Health, The Ohio State

University

2015...... M.S. Environment and Natural Resources,

The Ohio State University

2015 to present ...... Graduate Research Associate, Department

of Veterinary Preventive Medicine, The

Ohio State University

Publications

Cunningham, D., DeBarber, A.E., Bir, N., Binkley. L., Merkens, L.S., Steiner, R.D., and Herman, G.E. 2015. Analysis of Hedgehog Signaling in Cerebellar Granule Cell Precursors in a Conditional Nsdhl Allele Demonstrates an Essential Role for

Cholesterol in Postnatal CNS Development. Human Molecular Genetics. doi:10.1093/hmg/ddv042.

Fields of Study

Major Field: Comparative and Veterinary Medicine

Table of Contents

Abstract ...... ii

Acknowledgments...... iv

Vita ...... v

Publications ...... v

Fields of Study ...... vi

Table of Contents ...... vii

List of Tables ...... xiii

List of Figures ...... xvi

Chapter 1: Introduction ...... 1

Chapter 2: Literature Review ...... 8

Emerging Diseases from Wildlife: ...... 8

Virology and General Molecular Epidemiology of the Rabies Virus: ...... 17

Basic Virology ...... 17

Rabies Virus Life Cycle ...... 20

Transmission, Pathogenesis and Treatment ...... 23 vii

Molecular Methods for Identification of Rabies Virus and Distinguishing Variants 26

Rabies Virus Phylogeny in Africa ...... 43

Phylogenetic Analysis Summary and Applications...... 45

Ethiopia as a Hotspot for Emerging Infectious Disease:...... 47

Effects of Biodiversity on Infectious Disease ...... 47

Ethiopia as a Hotspot ...... 51

Basics of Rabies in Ethiopia: ...... 54

Modeling Basics and Obtaining Modeling Data from Wildlife: ...... 58

The Model-Based Approach ...... 58

Modeling Basics ...... 60

Allometrics as a Means to Simplify Parameterization ...... 72

Who Acquires Infection from Whom (WAIFW) Matrix Model ...... 74

Additional Methods to Consider When Examining Host-Species Heterogeneity in a

Multi-Host System According to Streicker et al. 2014 ...... 79

Network Models for Wildlife According to Craft and Caillaud 2011 ...... 81

Obtaining Modeling Data from Wildlife ...... 82

Multi-Species Infectious Disease Models for Wildlife Populations: ...... 87

Key Definitions...... 87

The Reservoir ...... 89

viii

Management Methods and when to Target the Reservoir ...... 91

Framework of Basic Theoretical Transmission Pathway Models ...... 92

Sources of Variation within the Continuum/ Stochasticity ...... 99

The Stochastic Model and Comparison ...... 101

Continuum Updated and Re-Explained ...... 103

Methods Used to Identify Reservoirs and Establish Transmission Dynamics ...... 107

Existing Scenario in the Serengeti Ecosystem: ...... 113

Reservoir Identification and Multi-Species Pathogen Study Designs ...... 113

Spatial Models for Multi-Species Pathogen ...... 123

Network Models to Examine Within-and-Between Species Transmission Rates ... 126

Spotted Hyenas of the Serengeti: Asymptomatic Rabies Carrier Theory and

Transmission Parameters ...... 130

Rabies Transmission in Southern Africa and Kenya: ...... 134

Background ...... 134

Mongoose Rabies ...... 137

Rabies in Wild Canids of South Africa: Black-Backed Jackal (Canis mesomelas) and

Bat- Eared Fox (Otocyon megalotis) ...... 143

Jackal Rabies Transmission in Zimbabwe ...... 151

A Different Perspective ...... 159

Identification of Other Lyssaviruses in Zimbabwe ...... 163

Rabies Transmission in Kenyan Carnivores ...... 164

What is Known about Transmission Dynamics of Rabies in Ethiopian Wildlife: ...... 168

Seasonality ...... 168

Summary of The Economy and Environment ...... 170

Rabies Transmission in Domestic Dog Populations...... 171

Rabies Transmission in the Ethiopian Wolf ...... 173

Rabies Transmission in Spotted Hyena (Crocuta crocuta) Populations ...... 176

Domestic –Wildlife Species Interactions ...... 179

Molecular Epidemiology of the Rabies Virus in Ethiopia ...... 184

Methods: How to Find out More About Contact Rates ...... 186

Proposed Scenarios for Rabies Transmission in Ethiopia ...... 190

Chapter 3: Molecular Epidemiology of Rabies in Ethiopia ...... 193

Abstract ...... 193

Introduction ...... 193

Materials and Methods ...... 197

Definitions ...... 197

Samples ...... 198

Results ...... 205

Discussion ...... 211

Conclusions ...... 216

Chapter 4: Identification of Contact Rates in Terrestrial Carnivores at Communal

Foraging Site in Ethiopia ...... 218

Abstract ...... 218

Introduction ...... 219

Methods ...... 222

Definitions ...... 222

Study Sites for Camera Trap Surveys...... 223

Camera Trap Selection and Setup ...... 225

Camera Trap Data Transcription and Analysis ...... 226

Results ...... 228

Discussion ...... 237

Conclusions………………………………………………………………………….241

Chapter 5: Rabies Maintenance Potential Within and Between Terrestrial Carnivore

Species Throughout Ethiopia ...... 243

Abstract ...... 243

Introduction ...... 243

Methods ...... 248

Site Selection ...... 248

Species Selection ...... 248

Modeling rabies transmission within species ...... 251

Determining if pairs of wildlife species could comprise a reservoir for rabies

maintenance ...... 254

Results ...... 258

Discussion ...... 264

Limitations ...... 264

Intraspecies R0 ...... 265

Interspecies R0 ...... 268

Conclusion ...... 270

Chapter 6: Conclusions ...... 272

References ...... 275

Appendix A. Intraspecies and Interspecies Contact Raw Data (Chapter 4) ...... 304

Appendix B. R0 Calculations (Chapter 5) ...... 326

Modeling rabies transmission within species ...... 326

Determining if pairs of wildlife species could comprise a reservoir for rabies

maintenance ...... 329

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List of Tables

Table 1. Latin Square ...... 71

Table 2 Who Acquires Infection from Whom Matrix ...... 75

Table 3.Who Acquired Infection From Whom Matrix with Scaling Factor for Within-

Stage Transmission ...... 76

Table 4. Epidemic Transition Matrix ...... 77

Table 5. Number of Samples per Species Selected for Sequencing of N-Gene ...... 200

Table 6. Number of Samples per Year Selected for Sequencing of N-Gene ...... 201

Table 7. Number of Samples per Location/Region Selected for Sequencing of N-Gene201

Table 8. Number of Complete N-Gene Sequences from the Year 2010 by Species ...... 201

Table 9. Number of Complete N-Gene Sequences from the Year 2010 by

Location/Region ...... 202

Table 10. GenBank Reference Sequences ...... 202

Table 11. Intra-Variant Average Pairwise Genetic Distance (P-Distance) from all

Variants Identified in the ML Tree Depicted in Figure 12...... 208

Table 12. Averaging Intergroup Pairwise P-Distance ...... 209

Table 13. Area (in m2) and Total Recording Time (in Minutes) for all Study Sites...... 225

Table 14. Within-Species Average of Maximum Contacts per Period by Species and Site.

...... 234

xiii

Table 15. Between-Species Average of Maximum Contacts per Period by Species and

Site...... 236

Table 16. Area of Selected Study Sites ...... 250

Table 17. Contact Rates Using 1 Day Time Steps for Each Species at Each Site/ Ci,i

Parameters ...... 253

Table 18. Parameterization of the SEIR to Represent Rabies Transmission in Ethiopia.

...... 254

Table 19. Contact Rates Using 1-Day Time Steps for Each Pair of Species at Each Site/

Ci,j Parameters ...... 257

Table 20. Intraspecies R0 ...... 260

Table 21. Interspecies R0 ...... 263

Table 22. Minutes Recording per Period at Each Site...... 304

Table 23. Maximum Number of Individuals Observed per Recording Period Addis Ababa

Slaughter Plant...... 305

Table 24. Maximum Number of Individuals Observed per Recording Period Goba

Slaughter Plant...... 306

Table 25. Maximum Number of Individuals Observed per Recording Period Awash

Slaughter Plant...... 306

Table 26. Maximum Number of Individuals Observed per Recording Period Awassa

Slaughter Plant...... 307

Table 27. Maximum Number of Individuals Observed per Recording Period Awassa

Waste Disposal...... 307

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Table 28. Maximum Group Size Across Periods by Species and Site...... 308

Table 29. Average of Maximum Number of Individuals Across Recording Periods by

Species and Site...... 309

Table 30. Addis Ababa Slaughter Plant Maximum Within-Species Contacts per Period.

...... 310

Table 31. Goba Slaughter Plant Maximum Within-Species Contacts per Period...... 311

Table 32. Awash Slaughter Plant Maximum Within-Species Contacts per Period...... 312

Table 33. Awassa Slaughter Plant Maximum Within-Species Contacts per Period...... 313

Table 34. Awassa Waste Disposal Facility Maximum Within-Species Contacts per

Period...... 314

Table 35. Addis Ababa Slaughter Plant Maximum Between-Species Contacts per Period.

...... 315

Table 36. Goba Slaughter Plant Maximum Between-Species Contacts per Period...... 316

Table 37. Awash Slaughter Plant Maximum Between-Species Contacts per Period. .... 317

Table 38. Awassa Slaughter Plant Maximum Between-Species Contacts per Period. ... 318

Table 39. Awassa Waste Disposal Facility Maximum Between-Species Contacts per

Period...... 319

List of Figures

Figure 1. Viral Structure...... 19

Figure 2. Rabies Genome ...... 19

Figure 3. Rabies Virus Lifecycle ...... 22

Figure 4. Time Course of Infection...... 63

Figure 5. Advanced Epidemic Transition Matrix for Calculation of R0 ...... 78

Figure 6. Examples of Target-Reservoir System Structure ...... 90

Figure 7. SI Models and the Four Outcomes for the Target Host Population ...... 96

Figure 8. The Fenton and Pedersen Continuum...... 99

Figure 9. Continuum updated by Viana et al. 2014 ...... 107

Figure 10. Potential Existing Scenarios for Existing Target-Reservoir Systems in the

Serengeti Ecosystem ...... 116

Figure 11. Map Showing the Distribution of Received Samples from EPHI ...... 200

Figure 12. Maximum Likelihood Tree of Partial N-gene Sequences and Reference

Sequences ...... 207

Figure 13. Maximum Likelihood Tree Ethiopia Complete N-Gene Sequences Compared to Somalia and the Sudan ...... 210

Figure 14. Camera Trap Survey Locations ...... 224

xvi

Figure 15. Species Rank-Abundance Curve for all Species Across all Sites Using

Maximum Group Size Within a Species Present at Each Site...... 230

Figure 16. Average Counts of Maximum Individuals Active per Hour at all Sites

Combined...... 232

Figure 17. Camera Trap Survey Locations ...... 249

Figure 18. Species Rank Abundance Curves Using Averages of Maximum Individuals.

...... 320

Figure 19. Addis Ababa Maximum Number of Active Individuals per Hour Over

Recording Period by Species...... 321

Figure 20. Goba Maximum Number of Active Individuals per Hour Over Recording

Period by Species...... 322

Figure 21. Awash Maximum Number of Active Individuals per Hour Over Recording

Period by Species...... 323

Figure 22. Awassa Slaughter Plant Maximum Number of Active Individuals per Hour

Over Recording Period by Species...... 324

Figure 23. Awassa Waste Disposal Facility Maximum Number of Active Individuals per

Hour Over Recording Period by Species ...... 325

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Chapter 1: Introduction

The rabies virus (RABV) causes fatal encephalomyelitis when it reaches the brain in all mammals (Velasco-Villa et al. 2008). Additionally, RABV has the highest human case-fatality proportion of any infectious disease (Hampson et al. 2009). Every year, over

7 million people receive post-exposure prophylaxis and an estimated 55,000 people die from rabies (Hampson et al. 2009). This multi-host virus is known to become compartmentalized by species and geographical area which leads to distinct virus variants that have establish sustained transmission networks (Lembo et al. 2007, Velasco-Villa et al. 2017, Fisher et al. 2018). As a result, multiple variants of the virus simultaneously circulate in different host species or, a single variant is maintained by multiple host species independently (Velasco-Villa et al. 2002, Lembo et al. 2007). The virus remains a great threat to public health worldwide due to the great diversity of rabies reservoirs, which has made prevention and control increasingly complex (Fisher et al. 2018).

Knowledge of all existing reservoirs and their transmission cycles is critical for control efforts.

Ethiopia has long been among the most rabies-affected countries in the world with a national annual incidence rate of 12/100,000 population rabies exposures and

1.6/100,000 population rabies deaths (Deressa et al. 2013). In a zoonotic disease prioritization workshop held as Ethiopia’s first step in engagement in the U.S. CDC

Global Health Security Agenda, rabies was identified as the number one priority disease

1 among 43 reviewed zoonotic diseases (Pieracci et al. 2016). However, knowledge of

RABV variants circulating throughout the country is scarce. Though wildlife in Ethiopia have been documented with rabies (Deressa et al, 2013, Johnson et al, 2010), little is known about the epidemiology of rabies in wildlife or the likelihood and frequency of spill-over to humans and domestic animals or vice-versa. Furtheremore, it has been extensively documented that long-term dog-maintained rabies epizootics may favor the establishment of dog-derived rabies virus variants in terrestrial carnivore populations thus augmenting rabies exposure sources for humans and domestic animals (Badrane et al.

2001, Bourhy et al. 2008, Velasco-Villa et al. 2008). This gap in knowledge is largely due to the fact that Ethiopia currently lacks wildlife disease surveillance. What is known comes predominantly from passive surveillance and is particularly disturbing: among wildlife and domestic species in and around Addis Ababa (excluding cats and dogs), roughly 60% of animals tested positive for rabies (Deressa et al. 2010). Between the years 1999 and 2000, 9 rabies positive spotted hyena samples were identified (Yimer et al. 2002) while 7 were identified between the years 2003 and 2009 (Ali et al. 2011). A study conducted in 1992 found that not only were samples submitted to the public health laboratory in Addis Ababa for testing found to be positive for rabies, but some were even found to be positive for other Lyssaviruses with no known treatment. The study identified one feline sample with Mokola virus and one domestic dog sample with Lagos bat virus

(Mebatsion et al. 1992).

People who traditionally link rabies with dogs rarely submit brain samples from other animal species. This underestimates the potential role of wildlife in rabies transmission due to surveillance bias (Reta et al. 2014). Additionally, rabies transmitted

2 by wildlife species could be a future challenge in the country when rabies transmitted by domestic carnivores is controlled (Nel et al. 2005, Reta et al. 2014). Despite vaccination campaigns and population control efforts for domestic dogs in targeted areas, such as the

Bale Mountains (Randall et al. 2006), rabies remains endemic in Ethiopia suggesting wildlife may play a role in rabies persistence or that there is re-introduction of dog- maintained rabies virus variants from neighboring regions. The interaction of wild and domestic animals in grazing areas, water points and backyard or slaughterhouse waste disposal areas can facilitate the circulation of the virus in the country (Deressa et al.

2010). Lack of information on the frequency of transmission in these aforementioned settings represents a critical gap in knowledge.

In South Africa and Zimbabwe, it is known that mongoose harbor an independent strain of the virus while the black-backed jackal and bat-eared fox sustain transmission independent of domestic dogs in particular geographical areas as long as certain ecological conditions exist. It is believed that these two species are showing the beginnings of new evolutionary branches and that if these branches manage to persist, they may become more distinct over time (Nel et al. 1993b, Sabeta et al. 2003, Sabeta et al. 2007, Zulu et al. 2009). In contrast, domestic dogs have been found to be the sole reservoir of rabies in Tanzania and Kenya (Cleaveland and Dye 1995, Lembo et al. 2008,

Prager et al. 2012). Cleaveland and Dye (1995) suggest that wildlife species do not maintain rabies transmission independently in these environments because they are species rich areas where no single carnivore species can reach a sufficient density for disease maintenance to occur. They also state that the predominance of domestic dogs among confirmed and reported cases in Tanzania and throughout Africa may be an

3 artefact of less intense surveillance and under-reporting of disease in wildlife (Cleaveland and Dye 1995). Throughout the world, most of the major wildlife reservoir hosts of rabies are opportunistic species that live at relatively high densities in agricultural areas or close to human settlements (Cleaveland and Dye 1995). As a country with nearly 102 million people where agriculture is the main source of income (United Nations Statistics Division

2016) and stable populations of such opportunistic species exist, Ethiopia has great potential for such sylvatic transmission cycles to be present. However, the situation remains unclear.

Identifying and managing all reservoirs of multi-host pathogens such as rabies plays a crucial role in effective disease control (Haydon et al. 2002, Viana et al. 2014).

This requires extensive knowledge of all existing RABV variants in circulation throughout the country. Dog vaccination campaigns alone cannot fully eliminate the threat of rabies to humans and domestic animal populations if wildlife reservoirs play an ongoing role in the transmission cycle. Understanding the current status of rabies in

Ethiopian wildlife becomes increasingly significant when dealing with endangered species, particularly the Ethiopian wolf. The Ethiopian wolf is the world’s rarest canid with an estimated population of fewer than 500 individuals limited to only seven isolated

Afro-alpine ranges across the Ethiopian highlands (Randall et al. 2006, Johnson et al.

2010). Six outbreaks of rabies from the years 1990-2009 have devastated the remaining populations and have been detrimental to conservation efforts (Marino et al. 2011).

This research seeks to identify RABV reservoirs throughout Ethiopia through examination of the large-scale genetic diversity of RABV circulating in domestic dogs along with the existence of alternative rabies cycles maintained by other mammalian

4 species. Objectives include: 1) Investigation of the genetic diversity of RABV circulating in wild and domestic species in Ethiopia through the use of molecular epidemiology; 2)

Identification of intraspecies and interspecies contact rates at communal foraging sites through the use of camera traps; 3) Application of contact rate estimates to mathematical expressions that will help determine maintenance potential through the use of disease modeling. This snapshot of rabies dynamics in Ethiopia provides important baseline data for prevention and control efforts. Findings can inform control policies and assist with strategies to appropriately target control efforts. Such findings promote RABV prevention through intervention at the human-wildlife-domestic animal interface, an interface that has been constantly growing as intense pressure from expanding agriculture is placed on remaining natural ecosystems (Randall et al. 2006, Johnson et al. 2010).

In the second chapter, I provide an intensive review of current literature relating to rabies transmission in Ethiopia including necessary background information on emerging disease from wildlife, virology of the rabies virus, Ethiopian ecosystems, disease modeling basics and transmission scenarios in other African countries.

In chapter 3, I utilized partial genome sequencing methods to examine the diversity of RABVs circulating in wild and domestic animal species. After confirmation of rabies positives through the use of RT-PCR (LN34), partial N- genes were sequenced from 187 samples collected throughout different regions of Ethiopia by the Ethiopian

Public Health Institute (EPHI) during the period 2012-2017. Complete N-genes for an additional 43 samples from the year 2010 were used from previous collaborations with

EPHI. We found an existing dog rabies epizootic consisting of a homogenous RABV variant that has been gradually spreading from an epicenter in Addis Ababa. This result

5 suggests movement of the canine RABV variant with no apparent boundaries. We also identified a RABV variant circulating in side-striped jackal populations of the Southern

Nations, Nationalities, and Peoples’ Region that shows 3.3% divergence from the dog rabies epizootic. This has the potential to lead to an independent cycle of transmission in wildlife if not controlled. Lastly, though we found a historical common origin for RABV circulating between Ethiopia, Somalia and the Sudan, we found no evidence of current dog-maintained RABV variant from other African countries.

In chapter 4, I used camera traps placed at communal foraging sites throughout

Ethiopia in order to identify contact rates (here co-occurrence will be synonymous with contact) within (intra) and between (inter) species. This was done in order to identify species that are high-risk transmitters of the rabies virus. We found that when contact rates were averaged across all sites, intraspecies contact rates were highest in domestic dog populations followed by spotted hyena and domestic cat populations, respectively.

Interspecies contact rates were highest between spotted hyena and domestic cat populations followed by interactions between domestic dogs and cats. Overall, interspecies contacts occurred much less frequently than intraspecies contacts.

In chapter 5, I applied contact rate estimates identified in chapter 4 to calculations of the basic reproductive number (R0) in order to determine the ability of these high-risk rabies transmitters to maintain independent transmission of the virus either within a single species or within a 2-species maintenance community. I found that the probability of independent maintenance for intraspecies transmission was highest in domestic dogs followed by spotted hyenas and domestic cats. The probability of interspecies maintenance within a 2-species maintenance community was highest

6 between spotted hyena and domestic cat populations and very low between other species.

Results highlight the need to further investigate the role of spotted hyena populations as reservoirs for RABV transmission throughout Ethiopia.

Chapter 2: Literature Review

Emerging Diseases from Wildlife:

The threat of zoonotic diseases to public health is increasing globally, particularly in tropical regions identified as major hotspots (Grace et al. 2012). Today,

60.3% of emerging infectious diseases are zoonotic, the majority of which originate in wildlife (71.8%) (Jones et al. 2008, Taylor et al. 2001). Pathogens originating from wildlife constituted 52% of major emerging infectious disease events worldwide from the years 1990-2000 (Jones et al. 2008). Increasing human populations, urbanization, and the introduction of large-scale commercial agriculture have changed the ecology of ecosystems allowing species that can exploit these changes to flourish in the new ecological landscapes in close proximity with humans and their domestic animals. Such species are the best candidate hosts for the maintenance of zoonotic pathogens (Bingham

2005). This increased contact creates opportunities for the transmission of endemic and newly emerging infectious diseases between livestock, wildlife and humans (FAO-OIE-

WHO 2010).

An emerging pathogen has been defined as “the causative agent of an infectious disease whose incidence is increasing following its appearance in new host population or whose incidence is increasing in an existing population as a result of long-term changes in its underlying epidemiology” (Woolhouse 2002). Disease emergence from wildlife most frequently results from a change in ecology of host, pathogen, or both (Daszak et al.

2000, Schrag and Wiener 1995). When examining disease emergence from wildlife, it is important to consider the fact that the emergence process involves ecological interactions at the individual, species, community and global scales (Childs et al. 2007). In this complex process, pathogens must become accommodated to or be accommodated by their reservoir hosts. The success of pathogen transmission in the reservoir is dependent on a multitude of circumstances such as the biology of the pathogen, the biology of the reservoir, the diversity of the reservoir species involved, the distribution within the geographical range of the reservoir species and the pathogen, and connectivity to other potential reservoir populations. Anthropogenic changes, such as clearing of forests, can then introduce both the pathogen and the reservoir to new ecological circumstances allowing them to expand into previously unavailable and unexplored niches where new opportunities for transmission exist (Childs et al. 2007). The transmission dynamics that result in zoonotic disease emergence from wildlife to human populations is complex and often times multifactorial. There are many ways to organize the emergence process for simplification. Childs et al. 2007 does an exceptional job by stating that there are two stages that must exist for emergence to occur and two additional stages that must exist for pandemic emergence to occur. They state that the two prerequisites for emergence include: 1.) Contact between infectious individuals of the wildlife host reservoir species with susceptible individuals of a secondary host species 2.) Successful cross-species transmission (spillover) in which the infectious cycle is able to be maintained within the secondary host. An intermediary may be required such as an arthropod vector or an intermediate vertebrate host in order for cross-species transmission to occur. Pandemic emergence requires these first two prerequisites in addition to another two prerequisites:

1.) Sustained transmission of the pathogen between members of the secondary host species independent of new spillover events 2.) Genetic adaptation and phenotypic changes accompany sustained intra-secondary host transmission. Once sustained transmission occurs within the new secondary host, evolutionary adaptation between pathogen and host can transform the once zoonotic virus into a distinctive new virus with a new host reservoir (Childs et al. 2007). Pathogens that fail to do this and require sustained re-introduction from animal reservoirs, such as rabies, are known as obligate zoonoses (Cleaveland et al. 2007a). Though these pathogens may not cause pandemic emergence, they can lead to outbreaks resulting in high morbidity and mortality. The likelihood of transition occurring and the extent of morbidity and mortality resulting from emergence is dependent on modifying factors (Childs et al. 2007).

Modifying factors can be abiotic or environmentally driven, intrinsic biotic/evolutionary, and extrinsic biotic factors (Childs et al. 2007). Abiotic factors mitigate the potential for contact between host reservoir and secondary host species or their infectious intermediates. For example, Clement et al. 2009 found that the emergence of Nephropathia epidemica (NE), a type of viral rodent-borne hemorrhagic fever that has existed in Europe for some time, was significantly influenced by increasing global temperatures. It was found that the higher and cyclic NE occurrence in Belgium and neighboring countries was associated with increased temperatures which in turn were associated with higher availability of the staple food for the rodent reservoir as well as a higher autumn-winter survival of the rodent (Clement et al. 2009). Both mast formation and winter survival of voles were found to be temperature-dependent to such a degree

10 that significant correlations appeared to exist, allowing reliable predictions of NE outbreaks based on climate parameters alone (Clement et al. 2009).

Intrinsic biotic and evolutionary factors include those factors that enhance the ability of the pathogen to cross species barriers or the susceptibility of the host to infection by a given pathogen (Childs et al. 2007). For example, viruses with high rates of replication and mutation as well as increased potential for recombination or reassortment may more readily adapt to new host species (Childs et al. 2007). Conversely, the pathogen exposure history and genetics of the host will help determine whether a pathogen is able to effectively establish infection within the host (Burgner et al. 2006).

Extrinsic biotic factors are factors working outside of natural systems that increase opportunities for disease transmission. For example, translocation of African rodents to the U.S. for the pet trade led to an outbreak of Monkeypox in the U.S. in the year 2003

(Centers for Disease Control and Prevention 2003). The presence of feral animals, a product of human activity, is another example. Feral animals act as conduits for pathogen exchange between otherwise isolated populations (Dobson and Foufopoulos 2001).

Almost all extrinsic factors are caused by anthropogenic change. Examples include habitat modification, human encroachment, modern agricultural practices, modern medicine, domestication of animals, urbanization and international travel among others

(Childs et al. 2007, Morse 1995). In most cases, emergence is influenced by a combination of modifying factors. One of the best examples comes from the emergence of the Nipah virus. In this case, the intensive pig farming industry in Malaysia resulted in human encroachment (extrinsic factor) that brought flying fox populations into close contact with humans and their domestic animals. Susceptible pigs (intrinsic factor)

11 acquired the virus through consumption of contaminated fruit dropped by the flying fox population into pig pens resulting in respiratory infection. Close contact of large numbers of humans with infected pigs due to farming practices resulted in viral transmission to human populations producing encephalitis in infected humans (Daniels et al. 2007). Each factor constitutes potential stopping points or “gates” within the transmission chain where intervention can be implemented or surveillance can be initiated in order to prevent such outbreaks (Daniels et al. 2007). This is why it is key to be able to identify high risk factors that cause emergence.

It is highly unlikely that we will ever be able to predict the exact timing and location that a pathogen will jump from one species to the next (Murphy 2008).

Mathematical models that attempt to predict the dynamics of emerging agents may be useful but can also blind us to increasing disease risks if the dynamics do not match a specific model (Murphy 2008). Field investigations of early emergence events will be critical (Murphy 2008) and as a result, identifying what emergence risk factors to look for should be a priority. One barrier that is faced when identifying risk factors for disease emergence is that they are often examined as broad categories such as climate change, human population increase, urbanization or habitat destruction (Cleaveland et al. 2007a).

These factors must be linked to specific effects on disease dynamics in order to target surveillance to the appropriate steps of transmission pathways (Cleaveland et al. 2007a).

Cleaveland et al. 2007a performed a literature search using the terms “factor” and

“emergence” combined with different pathogen names and summarized the results into conventional categories relating to epidemiological parameters such as contact rate and number of susceptible individuals. They found that risk factors that affect contact rate,

12 such as population density, habitat fragmentation and increased travel, were the most predominant factors. One of the major challenges they found when identifying risk factors was that emergence tends to result from multiple risk factors interacting simultaneously or sequentially. Additionally, broad risk factor categories do not always operate at a single specific step in the epidemiological framework but have the potential for multiple impacts on infection dynamics (Cleaveland et al. 2007a). This emphasizes the need to narrow down broad categories of risk factors into very specific effects and to examine how these factors interact with one another instead of analyzing each individually.

Identifying which pathogens are most likely to emerge is one major element that will help target resources. Cleaveland et al. (2001) constructed a database of disease- causing pathogen and found that pathogens that have the ability to infect more than one host, those that have the ability to infect more than one taxonomic order and those infecting wildlife hosts all have a higher relative risk for emergence than pathogens with more restricted host ranges. This same study found that among the taxonomic groups of pathogens, the proportion of viruses that are emerging is four times greater than other taxonomic groups (RR of emergence= 4.3). A similar study that surveyed emerging pathogens of wildlife recorded on the ProMED Web site for a 2-year period between

1998 and 2000 also found that the majority of pathogens recorded as causing disease outbreaks in wildlife were viral in origin (Dobson and Foufopoulos 2001). Among the viruses, RNA viruses are disproportionately represented among those pathogens that have emerged as new human and animal diseases after jumping from other host species

(Cleaveland et al. 2007a). Several factors that help explain why viruses appear

13 disproportionately among emerging pathogens include the relative difficulty of treating viral diseases, improved detection rates and short generation and higher mutation rates

(Cleaveland et al. 2007a). The over-representation of RNA viruses in instances of pathogens jumping into new host species can partially be explained by high mutation rates in RNA viruses as well as the existence of multiple variants within strains of RNA viruses all of which provide an enormous capacity for RNA viruses to adapt to changing host environments and to overcome barriers (Cleaveland et al. 2007a).

RNA viruses are thought to evolve much faster than DNA viruses as a result of rapid replication that is highly error-prone as well as large population sizes producing mutations that might be required to adapt to new environments (Holmes and Drummond

2007). However, there is still significant variation among RNA viruses in their ability to cause emergent disease. Identifying the causes of this variation can help determine risk factors for emergence. Holmes and Drummond (2007) suggest examination of several important factors. They state that the causes of variation in rates of evolutionary change among viruses need to be further examined. Additionally, they note that genetic drift and natural selection are important factors to consider. Because all viruses depend on the host cell machinery to complete their life cycle, the interaction between viral proteins and cellular receptors of host cells is of particular importance in determining why some RNA viruses are more often associated with cross-species transmission than others. For example, generalist viruses, which infect a broad range of cellular receptors, are more able to cross species boundaries than specialist viruses (Holmes and Drummond 2007).

Mode of transmission can help determine whether a virus can persist within a new population or not (e.g. viruses that rely on respiratory transmission may have more

14 opportunities for exposure to individuals within a population than other modes such as blood-borne transmission). Homes and Drummond 2007 describe an apparent association between mode of transmission and the ability of a virus to successfully replicate in the cells of a new host species using arboviruses as an example. They state that evidence from arboviruses suggests that the necessity of replicating in very different host species, mammal and arthropod in this case, imposes strong constraints against sequence changes necessary to establish infection in new hosts. More specifically, they state that this effect is most likely the results of an antagonistic fitness trade-off in which mutations that increase fitness in one host species reduce it in another. Consequently, the majority of amino acid changes that arise in either host are deleterious (or slightly deleterious) and removed by purifying selection (Holmes and Drummond 2007). This then limits the ability of the virus to successfully infect a new host and maintain transmission. Phylogenetic relationship plays a role in emergence as well. Reservoirs that have a close phylogenetic relationship are more likely to share related cell receptors as well as alleles that produce a similar immune response (Holmes and Drummond 2007).

Though recombination could play a role in emergence, Holmes and Drummond argue that it is unlikely that the role is significant. Other than in the retroviruses, recombination is not a common process in RNA viruses. For example, recombination appears to be extremely rare in negative-sense RNA viruses such as the rabies virus (Chare et al. 2003), most likely because their RNA is always encapsulated thus limiting template-switching ability (Holmes and Drummond 2007). One of the major questions that still needs to be answered is whether, following cross-species transmission, emergent viruses must adapt

15 to replicate in their new species, or whether they are already adapted once acquired as a product of natural selection (Holmes and Drummond 2007).

Overall the increasing threat of emerging infectious diseases of zoonotic origin emphasizes the need to target surveillance not just in humans, but in animal populations as well (Jones et al. 2008). Too often surveillance for disease emergence is largely restricted to identifying incident cases of disease in humans rather than monitoring infection and disease among wildlife populations (Childs et al. 2007). Identification and regular monitoring of sentinel wildlife populations associated with high-risk situations will be an important step. There is a growing need for more integrated ecological thinking within the human and animal health sectors (Cleaveland et al. 2014). Human health and wellbeing are fundamentally dependent on the flow of ecosystem services

(World Health Organization 2005a) however few human health initiatives include strategies related to ecosystem health and conservation of biodiversity. As a result, only proximate causes are often addressed thus producing short-term results (Cleaveland et al.

2014). Assessments of health risk should be accompanied by ecological risk assessments which address broader ecological health outcomes that include population survival, biodiversity and ecosystem health (Cleaveland et al. 2014). This can only be achieved through a truly One Health and interdisciplinary approach linking professionals in the fields of ecology, health, conservation, development, and animal welfare among others

(Cleaveland et al. 2014). The Joint FAO-OIE-WHO Global Early Warning System, a system that detects emerging risks at the human-animal-ecosystem interface, calls for such action stating that “It is critical that environmental and wildlife elements be addressed when examining the epidemiology and emergence of already known pathogens

16 behaving differently and new pathogens in wild animals, livestock and human populations to fully understand the drivers of these newly emerging infectious diseases to ultimately prevent or minimize the impacts…”

Virology and General Molecular Epidemiology of the Rabies Virus:

Basic Virology

The rabies virus causes fatal encephalomyelitis once it reaches the brain in all mammals, including humans (Velasco-Villa et al. 2008). It has the highest human case- fatality proportion of any infectious disease (Hampson et al. 2009). Every year, over 7 million people receive post-exposure prophylaxis and an estimated 55,000 people die from rabies (Hampson et al. 2009). In the developing world, someone dies every ten minutes from this 100% preventable disease (Hampson et al. 2009). The rabies virus is the prototype virus of the genus Lyssavirus (from Greek word lyssa meaning “rage”) in the family Rhabdoviridae (from the Greek word rhabdos, meaning “rod”) of the order

Mononegavirales (Wunner and Conzelmann 2013). There are fourteen lyssaviruses of which twelve can be divided into two phlylogroups. Phylogroup I includes the rabies virus, Duvenhage virus, European bat lyssavirus types 1 and 2, Australian bat lyssavirus, the Aravan virus, Khujand virus, Irkut virus and Bokeloh bat lyssavirus (Wunner and

Conzelmann 2013). Phylogroup II includes Lagos bat virus, Mokola virus and Shimoni bat virus. West Caucasian bat virus and Ikoma Lyssavirus, both isolated from an African civet, are thought to belong to a third phylogroup (Wunner and Conzelmann 2013).

Phylogenetic analyses suggest that all lyssaviruses have originated from a precursor bat virus (Wunner and Conzelmann 2013).

Lyssaviruses generally have a bullet-shaped morphology, helical nucleocapsid or ribonucleoprotein core and similar organization of the viral RNA genome and structural proteins (Wunner and Conzelmann 2013). The viral genome encodes five proteins including a nucleoprotein (N), a phosphoprotein (P), a matrix protein (M), a single surface glycoprotein (G) and an RNA-dependent polymerase or large protein (L) (Nel et al. 2005). A negative sense (negative mRNA must be transcribed into positive mRNA by own RNA polymerase before translation can occur), single stranded RNA forms the backbone of the tightly coiled helical RNP ribonucleoprotein (RNA plus protein) core.

This core extends along the longitudinal axis of the bullet-shaped virus particle. The single-stranded RNA is encapsidated by the protein components, N, P, and L. These proteins are surrounded by the viral membrane proteins, M and G, and a mixture of lipoproteins derived from the cell membrane that form the outer envelope or “membrane matrix” of the virion (Wunner and Conzelmann 2013) (Figure 1). The rabies viral genome is approximately 12 kbp in length with the five viral genes arranged in a strictly conserved order (3′-N-P-M-G-L-5′) and flanked by short terminal regulatory sequences

(Wunner and Conzelmann 2013) (Figure 2).

Figure 1. Viral Structure.

(Image taken from Jackson, A. C. (Ed.), Rabies: Scientific basis of the disease and its management, 17-60, San Diego, CA: Elsevier)

Figure 2. Rabies Genome

(Image taken from Centers for Disease Control and Prevention. 2011. The Rabies

Virus. Retrieved 2017 from: https://www.cdc.gov/rabies/transmission/virus.html )

The amino acid sequence of the nucleocapsid protein is the most conserved of the lyssavirus viral proteins however, there is a relatively high degree of genetic diversity within short segments of the N-gene between species (Wunner and Conzelmann 2013).

These amino acid differences result in species-specific epitopes on the nucleocapsid that play a key role in defining antigenic relationships between virus strains within and between species. For example, monoclonal antibody typing (MAb) is based on reactivity patterns (antigenicity) with a panel of anti-N monoclonal antibodies (Wunner and

Conzelmann 2013). The diversity in the N gene at the nucleotide level has also made extensive analysis of phylogenetic relationships of lyssaviruses possible and has led to suggested quantitative criteria for lyssavirus species definitions using the polymerase chain reaction (PCR) and nucleotide sequencing technologies (Wunner and Conzelmann

2013). The L-gene is the second most conserved sequence while the P and G genes are more variable. The M gene exhibits intermediate variability. Variation in similarity within each gene further identifies specific protein coding regions or domains that are more or less conserved (Nadin-Davis 2013). For example, highly variable and conserved domains of the P-gene have been identified and the central region of the N-gene is known to be less variable than either the N- or C-terminus of N. The non-coding G-L intergenic region is the most variable region of the genome (Nadin-Davis 2013).

Rabies Virus Life Cycle

In general, the sequence of events in the rabies virus life cycle can be divided into three phases. The first phase includes virus attachment to receptors on susceptible host cells, entry via direct virus fusion externally with the plasma membrane and internally with endosomal membranes of the cell, uncoating of virus particles and release of the

20 helical rabies nucleoprotein into the cytoplasm. The second phase includes transcription and replication of the viral genome and viral protein synthesis. The third phase includes virus assembly and emergence from the infected cell (Wunner and Conzelmann 2013). In slightly more detail provided by the Centers for Disease Control and Prevention (2011), after adsorption, the virus penetrates the host cell and enters the cytoplasm where the virions aggregate in the large endosomes. After the viral membranes fuse to the endosomal membranes, the viral RNP core is released into the cytoplasm in a process known as uncoating. Once uncoated, the messenger RNAs (mRNAs) must be transcribed by the RNA polymerase (L gene). The polymerase transcribes the genomic strand of

RNA into leader RNA and five matured (capped and polyadenylated) mRNAs, which are then translated into proteins on free ribosomes in the cytoplasm. Though G protein synthesis is initiated on free ribosomes, completion of synthesis and glycosylation occur in the endoplamsic reticulum (ER) and Golgi apparatus. The intracellular ratio of leader

RNA to N protein regulates the switch from transcription to replication. When this switch is activated, replication of the viral genome begins (Centers for Disease Control and

Prevention 2011) (Figure 3).

The first step in viral replication is synthesis of full-length, positive strand, copies of the viral genome. These positive strands of rabies RNA serve as templates for synthesis of full-length negative strands of the viral genome (Centers for Disease Control and Prevention 2011). During the assembly process, the N-P-L complex encapsulates negative-stranded genomic RNA to form the RNP core, and the M protein forms a matrix around the RNP. The RNP-M complex migrates to an area of the plasma membrane containing glycoprotein inserts where it binds with the glycoprotein causing the

21 completed virus to bud from the plasma membrane (Centers for Disease Control and

Prevention 2011).

Figure 3. Rabies Virus Lifecycle

(Image taken from Centers for Disease Control and Prevention. 2011. The Rabies

Virus. Retrieved 2017 from: https://www.cdc.gov/rabies/transmission/virus.html )

Transmission, Pathogenesis and Treatment

The rabies virus can be transmitted in the saliva, tears and cerebrospinal fluid.

Contact with other bodily fluids, such as blood, are not considered to be rabies exposures

(Hanlon and Childs 2013).The most common route of rabies virus exposure and transmission is through an animal bite (World Health Organization 2005b). The risk of developing rabies depends on the anatomical site and severity of the bite, the species inflicting the wound, and the rabies virus variant (Hanlon and Childs 2013).

Contamination of fresh, open wounds with infectious material is another means of exposure to rabies virus (Hanlon and Childs 2013). Contamination of mucous membranes is a much less effective route of potential exposure to rabies than through bites or open wounds. Other less common routes of exposure include ocular exposure through transplantation of corneas from humans dying of rabies, iatrogenic human-to-human transmission to recipients of rabies infected transplanted human tissues, and unpasteurized milk from rabid cows (Hanlon and Childs 2013). Interestingly, rabies virus infection is thought to have been acquired by droplets of aerosolized virus in two people that developed rabies after visiting Frio Cave in Texas (Irons et al. 1957). Researchers investigated the potential for aerosol transmission of rabies to caged animals (mostly canids) held in caves and concluded that aerosol transmission of rabies virus was possible, but only in a few U.S. caves with very large bat colonies coupled with extreme humidity, high temperature, and poor ventilation (Messenger et al. 2002).

The timing of the progression of viral infection from local neurons at the site of infection to the central nervous system of an infected host is currently unpredictable

(Hanlon et al. 2013). There is a great amount of variability in incubation period both among and within species. The incubation period is generally inversely related to viral dose and severity of exposure, proximity to the brain, the species of the animal, and the variant of the virus (Hanlon 2013). In humans, the incubation period is typically 1–3 months but may vary from <1 week to >1 year (World Health Organization 2016). The majority of infected animals become clinically rabid within several weeks to several months following exposure (Hanlon 2016). On the basis of experimental and field data, a

6-month quarantine is imposed on exposed, unvaccinated (or previously vaccinated but out-of-date) domestic animals, as this is considered a probable maximum incubation period (Hanlon 2013). Once the virus reaches the central nervous system infection spreads quickly through numerous neuronal tracts. As a result, the clinical illness is relatively brief usually lasting several days rather than weeks (Hanlon 2013). Once the brain is infected, virus is then capable of spreading centrifugally from the central nervous system through the innervation of major organ systems. Virus is also produced in high amounts in the salivary glands which leads to the presence of virus in the saliva (Hanlon

2013).

Initial symptoms include general lethargy, poor appetite and diarrhea or vomiting

(Hanlon 2013). There are two forms of the disease; furious and paralytic (World Health

Organization 2016). With the furious form, people or animals may exhibit signs of hyperactivity, excited behavior, hydrophobia and sometimes aerophobia (fear of flying)

(World Health Organization 2016). Animals may become more reclusive or attention- seeking than normal or they may unpredictably and intermittently attack animate

(humans or other animals), inanimate, or unseen objects (Hanlon 2013). A combination

24 of increased saliva production and a decreased ability to swallow may results in contamination of the mouth, chin and forelegs with potentially infectious saliva. Cranial nerve involvement may be focal and unilateral, presenting as unequal pupil size with dys- function, facial or tongue paresis, and changes in phonation. As the clinical period progresses, unpredictable episodes of attempts to bite may be invoked by auditory, visual, or tactile stimuli with aggression to the point of self-mutilation (Hanlon 2013). After a few days, death occurs by cardiorespiratory arrest (World Health Organization 2016).

Paralytic rabies accounts for about 30% of the total number of human cases (World

Health Organization 2016). This form of rabies is generally less dramatic and runs a longer course than the furious form. The muscles gradually become paralyzed, starting at the site of the bite or scratch. A coma slowly develops, and eventually death occurs

(World Health Organization 2016).

Rabies post-exposure prophylaxis treatment in humans in the United States consists of a series of four rabies vaccine inoculations administered on days 0, 3, 7 and 14 in combination with administration of human anti-rabies immunoglobulin on the first day of vaccination (Centers for Disease Control and Prevention 2016). In general, modern cell culture derived vaccines, when administered with anti-rabies immunoglobulin, are

100% effective in preventing rabies after an exposure has occurred (Hanlon and Childs

2013). In 2001, the WHO issued a resolution for the complete replacement of nerve tissue vaccines by 2006 with cell-culture rabies vaccines (Hurisa et al. 2013). In Ethiopia however, nerve tissue derived vaccines are still being used (Hurisa et al. 2013).

Molecular Methods for Identification of Rabies Virus and Distinguishing Variants

Direct Fluorescent Antibody Test (DFA):

Fluorescence microscopy and fluorescein-labeled antibodies led to the creation of the Direct Fluorescent Antibody Test which is now the “gold standard” diagnostic technique used for rabies diagnosis (World Organization for Animal Health 2011, World

Health Organization 2005b). This method is both highly sensitive (probability of a positive test in an individual known to have the disease; true positive rate; more likely to produce a false positive) and specific (probability of a negative test in an individual known to not have the disease, true negative rate, more likely to produce a false negative). Dean et al. 1996 reported that from 1966 to 1970, nearly 15,000 specimens were examined in the Laboratories for Veterinary Science of the New York State

Department of Health, Albany, NY, USA. Of these, 802 of a total of 804 were found positive by the DFA test compared to mouse inoculation tests. The two cases where the mouse test produced a positive and the DFA test produced a negative are thought to be due to insufficient sampling (Dean et al. 1996). Meslin and Kaplan 1996 reported that after one year of experience with the DFA method, most laboratories find over 99% agreement between the DFA test and the mouse inoculation test.

In this method, antibodies to epitopes found on the nucleocapsid protein of rabies viruses are fluorescein-conjugated. When these antibodies bind to proteins in infected neurons, fluorescent foci are observed under fluorescence microscopy (Hanlon and

Nadin-Davis 2013). The main limitation of this method is the need for a high quality fluorescence microscope and highly trained personnel (Hanlon and Nadin-Davis 2013).

Additionally, it requires that the conjugate be working properly so as not to produce non- specific staining. One of the major benefits of this method, in addition to its extreme sensitivity and specificity, is that it can produce results with a quick turnaround time.

Usually results can be produced in a matter of several hours. Because multiple slides can be tested at once, DFA also has a moderately high throughput. In the United States, a committee was formed from representatives of national and state public health laboratories to evaluate procedures employed by rabies diagnostic laboratories across the country (Hanlon and Nadin-Davis 2013). The committee identified and described the minimum standards for reliable rabies diagnosis in the USA (Anonymous 2003).

Currently there is a need to establish best practices in regard to method validation within each laboratory (Hanlon and Nadin-Davis 2013).

Direct Rapid Immunohistochemical Test (DRIT):

Another method that is often used as a confirmatory test for DFA results and is now being tested in the field as an early screening method is the direct rapid immunohistochemical test (DRIT). This method was found to have 100% specificity and sensitivity when compared to DFA as long as proper protocol is followed (Lembo et al. 2006, Durr et al. 2008). This method relies on touch impressions of brain material using a combination of biotinylated anti-nucleocapsid monoclonal antibodies (Lembo et al.,

2006). The material is then incubated with streptavidin-peroxidase complex, followed by an AEC peroxidase substrate, and then counterstained with hematoxylin. Results can be examined by normal light microscopy (Hanlon and Nadin-Davis 2013). This method is not used as a primary diagnostic tool largely because the primary reagents are not reliably available at this time due to difficulty with conjugate reactivity.

Polymerase Chain Reaction and Reverse Transcriptase Polymerase Chain Reaction:

Polymerase Chain Reaction technology has developed into a highly sensitive method for identification of rabies virus and distinguishing among variants. This method relies on the use of two synthetic oligonucleotides (short sequences of nucleic acid) known as primers that are complementary to opposite strands of dsDNA and can hybridize at target sequences. They are oriented in such a way that when they prime new

DNA synthesis, the newly created DNA strands overlap in sequence (Hanlon and Nadin-

Davis 2013). One primer is called the forward primer and moves from the 5’ to the 3’

(left to right) end of a single stranded DNA template while the other primer, the reverse primer, moves from the 3’ to the 5’ (right to left) end of the opposing single stranded

DNA template. The reaction is catalyzed by a thermostable DNA polymerase which requires thermocycling to repeatedly denature the dsDNA, anneal the oligonucleotide primers to their respective single-stranded DNA templates and extend the primers via

DNA polymerase thus duplicating the DNA fragment between the primers. The targeted

DNA fragment is then amplified exponentially with each cycle. The amplification of this product, usually by more than 100,000 fold, is the basis for the assay’s exquisite sensitivity (Hanlon and Nadin-Davis 2013). One of the most important steps to achieve successful results is proper primer design. This involves selecting primers that are 15-20 nucleotides in complementary regions, have random base distribution with average

Guanine-Cytocine content, have base pairing targeted at 3’ ends, avoid Adenine-Thymine and Guanine-Cytosine-rich regions, and primer 3’ ends that are not complementary to

28 each other thus causing secondary structures such as hairpins. These are only a few of the general considerations that must be taken into account.

Designing broadly reactive PCRs for detection of all members of the Lyssavirus genus is complicated due to genetic variation between and within species. In order to develop a robust, broadly cross-reactive assay, careful primer design is needed to ensure that the primers bind to sequences well conserved across the viral group targeted (Hanlon and Nadin-Davis 2013). When selecting a target sequence and designing primers, one must consider that Lyssavirus mutation almost always occurs via single base substitutions or through small insertions/deletions in non-coding regions. Within protein coding regions, the majority of mutations are third base synonymous changes (codes for same amino acid), although first and second base changes are also observed. Second base changes are always non-synonymous (Hanlon and Nadin-Davis 2013). When selecting target sequences for a broadly reactive PCR, one should avoid sites where these common mutations occur.

To apply PCR to lyssaviruses, the viral RNA must first be converted to a complementary DNA (cDNA) strand using the enzyme reverse transcriptase. Reverse- transcriptase polymerase chain reaction (RT-PCR) assay, which allows generation of dsDNA copies of portions of the viral genome, remains a pivotal method for genetic characterization of lyssaviruses (Nadin-Davis 2013). RT-PCR is the most frequently employed molecular method when seeking to detect rabies and rabies-related virus RNA

(Hanlon and Nadin-Davis 2013). This method is generally applied to post-mortem brain stem or cerebellar material from a suspect rabid animal or human however it can also be applied to saliva, skin samples, salivary glands, and almost any other tissue. When

29 amplification is successful, sequence characterization of the product and comparison with other reference viruses can identify the variant type, in what species it is most commonly transmitted and from which geographic area closely related viruses circulate (Hanlon and

Nadin-Davis 2013). However, the reverse transcription step is often the limiting step when amplifying RNA virus sequences because it usually inhibits amplification of lengths greater than 5 kb in a single reaction (Hanlon and Nadin-Davis 2013). One of the benefits of this method, despite the increased possibility for producing false positives, is its extreme sensitivity. Sensitivity for this method was found to be 100% when comparing 75 field samples of animal brain to DFA results (Durr et al. 2008).

Additionally, RT-PCR was shown to be 1.6 times more sensitive than virus isolation by

Panning et al. 2010.

Nucleotide Sequencing:

Direct nucleotide sequence determination applied to reverse transcription PCR products is the most commonly applied method for both DFA confirmatory testing and genetically comparing a collection of lyssaviruses (Hanlon and Nadin-Davis 2013). Most currently used methods are based off of a method known as Sanger sequencing or the chain-terminating method. In general, this method relies on selective incorporation of fluorescently labeled dideoxyribonucleotides (ddNTPs) by a mutated TAQ polymerase that has low discrimination against the ddNTPs (Hoet 2016). Dideoxyribonucleotides lack a 3’ hydroxyl group that is required for formation of a phosphodiester bond between two nucleotides thus causing the extension of the DNA strand to stop when a ddNTP is added. In order to determine the sequence of the desired DNA template, four reactions take place, one for each ddNTP (ddATP, ddGTP, ddCTP, ddTTP). For example, the

30 ddATP reaction produces a nested set of single-stranded, randomly terminated DNA extensions of different length all ending in “A.” Due to the proportions of the reagents in the reaction, it is assumed that all possible segment lengths ending in “A” are produced.

Therefore, this reaction will indicate where each “A” occurs in the sequence. The same reaction is carried out with the remaining ddNTPs and the products are then run out on a gel. The fragments are separate by size through electrophoresis and as each fragment runs off of the gel, a laser reads the fluorescently labeled ddNTP associated with that segment.

Once all of the segments have run off, a complimentary sequence to the DNA sequence of interest is produced (Hoet 2016). This method has been modified so that instead of having to carry out four separate reactions, sequencing can be complete in one reaction by labeling the ddNTPs with fluorescent dyes of different wavelengths. This method is known as the dye-terminator sequencing method and relies on capillary electrophoresis to detect the different wavelengths. When using dye-terminator sequencing, the final sequence must be edited and compared to other sequences of the same target produced from different specimens. The consensus sequence is then determined by aligning these nucleotide sequences with one another and identifying the most frequently expressed nucleotide at each position (Hoet 2016). Sanger sequencing, and variation of this method, are considered to be the gold standard sequencing method since the 1970’s (Bergland et al. 2011, Hoet 2016). The major limitation of this method is that it can generally only sequence fragments between 300-1000 bp in a single reaction which limits throughput.

Newer next generation technologies have been developed that produce much higher throughput however, these methods have not yet replaced Sanger sequencing methods as the gold standard.

Sanger sequencing methods are also the best to use for degraded samples (A.

Velasco-Villa, personal communication, January 30’th 2017). By using gene specific reverse transcription-PCR, relatively short fragments can be amplified which permits getting a whole gene, such as the N gene, sequenced in two or three amplicons. These

600 to 850 bp long amplicons are then further sequenced by Sanger sequencing methods using their respective forward and reverse primers (A. Velasco-Villa, personal communication, January 30’th 2017). Detection of rabies virus in decomposed samples by RT-PCR has been demonstrated in Ethiopian wolf carcasses (Johnson et al. 2010).

Though whole genome sequencing has become very popular in recent years, this method is not ideal for purposes of rabies phylogenetic analysis at this point. This is due to the fact that there is very little data available on whole genomes of the rabies virus throughout the world so there is no means for comparison. The N-gene is the most commonly sequence gene worldwide and there are many sequences to compare with thus providing a robust representation of the global diversity of the rabies viruses (13,002 sequences in GenBank [www.ncbi.nlm.nih.gov/genbank/). As a result, N-gene sequences can help make more robust global comparisons with rabies viruses or lyssaviruses that may be circulating in a region or country. Such robust information contributes to identification of reservoir host species or specific rabies cycles as well as existing or novel Lyssavirus species. Other genes, although equally informative, have a very poor grasp of such global diversity. Their use may limit the robustness of comparisons (A.

Velasco-Villa, personal communication, January 30’th 2017). In regard to whole genome sequencing, there is currently not a standard algorithm to get full genome sequences of lyssaviruses. Additionally, whole genome sequencing methods generate millions of reads

(contigs) which must be analyzed, edited and assembled using tailored analytical methods often created by expert bio-informaticians and computer programmers (A. Velasco-Villa, personal communication, January 30’th 2017).

Antigenic Typing:

Antigenic typing is a method that relies on patterns of reaction to panels of monoclonal antibodies (MAbs). The discriminatory power of this method lies in the ability of each MAb of the panel to react with a specific epitope of a viral protein. If the epitope is present, the MAb reactivity will be scored as positive and if it is not present then no reaction will occur and the MAb reactivity will be scored as a negative (Nadin-

Davis 2013). Considering each MAb binds to only one epitope, the discriminatory power of the panel can be improved by targeting multiple independent epitopes. Selection of an appropriate MAb panel is dependent on the viral populations to be targeted within a specific geographic region. Therefore, a MAb panel developed for use in one area may not be useful for strain typing in other areas (Nadin-Davis 2013). The nucleoprotein is the viral protein that is most commonly targeted by this method because it is produced in large quantities in infected brain tissue and provides an abundant target. Binding of a

MAb to a viral protein is usually assayed by an indirect fluorescent antibody test. The variant type of an unknown isolate can generally be determined by comparing its MAb reactivity profile with the reactivity profiles of representative reference specimens.

However, it should be noted that if two genetically distinct but closely related viral variants encode similar viral proteins, antigenic typing will not be able to discriminate between them (Nadin-Davis 2013).

Strain-Specific PCRs and Probes:

Strain-specific PCR relies on primers that target sequences known to show variability among strains (Hanlon and Nadin-Davis 2013). By adjusting annealing conditions so that these primers hybridize selectively only to their matched target sequence, a PCR product that is specific for a particular strain can be generated.

Amplicons of different sizes indicate sequence variability allowing the sequences to be compared. Probes work similarly by highlighting the target sequences for comparison

(Hanlon and Nadin-Davis 2013).

Real Time/Q-PCR:

The development of real-time PCR has provided the technology necessary to develop highly sensitive, rapid and quantitative assays for lyssaviruses (Hanlon and

Nadin-Davis 2013). While real-time PCR utilizes the same principles as standard PCR, in which two PCR primers generate an amplicon, the detection of this product is enhanced during the course of the reaction by linkage to increasing levels of fluorescence that are converted into an amplification plot (Hanlon and Nadin-Davis 2013). Any sample in which the fluorescent signal rises significantly above the applied threshold prior to the end of the run is considered positive. The cycle at which the fluorescence statistically rises significantly above background levels, known as the critical threshold cycle number, can be used to estimate the amount of target sequence present in the reaction in comparison to values obtained for a series of standards of known amount (Hanlon and

Nadin-Davis 2013). This method is highly sensitivity due to both the use of fluorescent dyes for product detection and the targeting of relatively short amplicons, between 80–

200 bp (Hanlon and Nadin-Davis 2013). In the case of RNA viruses, which require reverse transcription prior to the PCR, both one and two step assays have been developed.

The two-step assay requires a separate reverse transcription reaction prior to the PCR while the one step assays rely on enzyme mixtures that permit both the reverse transcription reaction and the PCR to be performed sequentially within the thermocycler

(Hanlon and Nadin-Davis 2013).

Sensitivity and specificity of this method depend on use of either the TaqMan or

SYBER green methods. The TaqMan method is dependent on a dual-labeled probe

(DLP). This probe is made of a synthetic oligonucleotide, which binds to internal sequence on one strand of the amplicon, and is labeled at both the 5’and 3’ends. The 5’ end of the DLP is covalently bonded to a reporter dye that fluoresces when irradiated at a certain wavelength. A quencher is covalently bonded to the DLP’s 3’-end so that emissions by the reporter dye are effectively quenched (Hanlon and Nadin-Davis 2013).

When the target sequence is present in the PCR, the DLP binds to its cognate sequence during the annealing step. During DNA synthesis, the 5’ nuclease activity of the DNA polymerase degrades the probe, the reporter and quencher become dissociated from each other, and the presence of PCR product is detected as fluorescence emitted by the reporter dye at a defined wavelength (Hanlon and Nadin-Davis 2013). Levels of this signal rise during the course of the reaction until a plateau is reached once the reagents in the reaction are used up. If no amplicon is produced during the PCR, the DLP cannot bind to amplicon and it remains intact thus emitting no signal and the sample is scored as negative (Hanlon and Nadin-Davis 2013). The SYBER green method utilizes DNA- intercalating dye. As amplicon is produced by the two PCR primers, increasing amounts of the dye are expressed with the product resulting in increasing levels of fluorescence

(Hanlon and Nadin-Davis 2013). Therefore, the dye increases with increasing amplicons.

One of the major advantages of Real-Time PCR (Q-PCR) is its quantitative nature. It can provide measurements of actual viral load present in a sample. This method has been used to determine post-mortem levels of virus present in various tissues of infected humans or animals and to determine titer of virus present in saliva samples from patients undergoing experimental treatment (Hanlon and Nadin-Davis 2013). Studies have shown that this method is even more sensitive than virus isolation (Pannin et al. 2010).

Restriction Fragment Length Polymorphism:

Restriction Fragment Length Polymorphism (RFLP) is a method that has been used many times to distinguish rabies virus variants. For example, it has been used to identify variants of the arctic fox virus that circulate in geographically restricted areas of

Canada (Nadin-Davis et al. 1994). This method uses the same restriction enzyme to restrict DNA fragments from different samples in the same place. These fragments are then separated using horizontal gel electrophoresis. Once the fragments have been separated, they are blotted to a carrier and hybridized with a repeat elements probe of known repeat sequences. The probe highlights the repeats in each variant so that the frequency and placement of repeats can be compared (Gebreyes 2016). This method is generally referred to as a Southern Blot however there is also a PCR-based RFLP that is less commonly used. This method amplifies targeted loci of the genome and then uses restriction to identify specific gene targets. The purpose is to determine presence/absence of the gene as opposed to distinguishing isolates (Gebreyes 2016).

Heteroduplex Mobility Assay:

The heteroduplex mobility assay (HMA) is a fairly simple assay that relies on the annealing of two PCR products of the same length, (for rabies PCR products are

36 generated from the N genes) generated from two different specimens and then assessing the change in mobility of the resulting products by standard gel electrophoresis (Nadin-

Davis 2013). The greater the genetic difference (or differences in base pairs) between the two products, the greater the degree of retardation of the heteroduplex (recombination of single complementary strands derived from different sources) compared to the homoduplex (product with precise complemantary bases in the two strands of DNA).

This method was used to discriminate between PCR amplicons generated from a collection of rabies viruses recovered in Turkey. The researchers were able to identify three main viral lineages through the use of this assay that were subsequently confirmed by sequence analysis (Johnson et al. 2003). The authors conclude that HMA offers a low- cost alternative to identifying strains of rabies virus that is rapid and can be used on large numbers of isolates in association with standard PCR techniques (Johnson et al. 2003).

Other Methods:

Immunohistochemistry on formalin-fixed paraffin embedded tissues can be used to identify rabies presence or absence (Hanlon and Nadin-Davis 2013). This method requires a significant amount of time and expertise compared to other methods however, if the tissue is fixed, this may be one of the only options for rabies identification. Another method that was used more frequently in the past is the mouse inoculation test which allows isolation of the virus from infected nueroblastoma cells (Meslin and Kaplan

1996). This requires intracerebral inoculation of a brain homogenate into young mice or into cell culture. However, if the homogenate contains a pathogen that causes mice to succumb or infects cell cultures, additional methods to confirm the nature of the pathogen are required (Hanlon and Nadin-Davis 2013).

Advantages and Disadvantages of Molecular Methods:

One of the greatest advantages of molecular-based methods is their high sensitivity (Hanlon and Nadin-Davis 2013). However, this can increase the potential for false positive results due to sample contamination that can easily occur when amplification methods are applied without great attention to detail (Hanlon and Nadin-Davis

2013). In relation to the high sensitivity of these methods, another advantage is their ability to detect virus in samples that are highly decomposed. Controlled observations have shown that in such a situation, molecular methods of detection can be significantly more sensitive than alternative methods (David et al. 2002, Hanlon and Nadin-Davis

2013). These methods are also especially useful for confirmatory testing of DFA negative results due to quick production of results and high sensitivity (Hanlon and Nadin-Davis

2003).

One disadvantage of using molecular-based methods is that for lyssaviruses, variation at the level of the nucleic acid genome is significantly greater than at the protein level (Hanlon and Nadin-Davis 2013). As a result, failure to detect a virus present in a sample (false negative) is potentially a larger problem using molecular methods (despite their high sensitivity), which usually rely on the hybridization of relatively short oligonucleotides to the RNA target compared to antibody-antigen binding strategies that form the basis of the DFA and related methods (Hanlon and Nadin-Davis 2013). In other words, the epitopes detected by an antibody are more likely to be conserved than a particular nucleotide sequence. Therefore, if trying to identify a particular sequence and

38 unable to do so, it might be because of the variation of the nucleic acid genome

(compared to the more conserved protein level) rather than an actual negative result.

Phylogenetic Analysis:

Phylogenetic analysis relies on direct nucleotide sequencing applied to reverse- transcription PCR products over a predetermined sequence window in order to genetically compare a collection of lyssaviruses. Alignments of such sequence data allow direct comparison of isolates to reference sequences and hence allocation to a particular type (Nadin-Davis 2013). This type of analyses requires enough sequence information from different sources in order to generate phylogenies with sufficiently robust statistical support. Comparison of an unidentified viral sample with viruses representative of a region not only requires a large amount of sequence data from that region, but also requires that the sequences be characterized over the same portion of the genome (Nadin-

Davis 2013). Even though it has been observed that similar conclusions on the overall epidemiological relationships of particular lyssaviruses are obtained regardless of a specific gene, a portion of a gene, or the whole viral genome (Wu, Franka, Velasco-Villa

& Rupprecht 2007), the majority of comparative rabies virus studies have employed the

N gene, or parts thereof (Nadin-Davis 2013). Most of these data are now widely accessible through the GenBank sequence database maintained by the National Center for

Biotechnology Information (NCBI, n.d.), National Institutes of Health in the USA. While databases for other genes (G and P) and whole genomes are now increasing in scope, the

N gene repository is currently by far the most extensive (Nadin-Davis 2013).

In general, longer target sequences have more discriminatory power. Short target sequences (200–300 bases) can yield phylogenetic relationships, however the longer a

39 sequence window, the greater the likelihood of finding differences between samples

(Nadin-Davis 2013). Additionally, the number of informative characters available to a phylogenetic analysis increases with increasing genetic variation. Therefore, for studies that seek to monitor variation within a closely related viral population, determination of a longer sequence window (>500 bases) or a relatively variable region of the genome, or both, is the most effective approach to show relationships that are strongly supported by statistical analyses (Nadin-Davis 2013). The use of a phylogenetic tree is beneficial when comparing large numbers of isolates, especially those with long sequences of nucleotides.

A phylogenetic tree is simply a diagram of hierarchical branches that indicates the evolutionary relationships between samples according to their nucleotide substitution patterns (Nadin-Davis 2013). Phylogenetic tree construction relies on computer programs that use a variety of different algorithms and methods.

One such method is the neighbor joining method. In these phylogenetic trees, samples that form a discrete cluster on one branch of the tree are said to form a clade.

When there is strong support for such a cluster the samples are said to form a monophyletic clade which indicates that all members originated from a common precursor (Nadin-Davis 2013). More specifically, this method converts aligned sequences into a distance matrix of pairwise differences between the sequences (Hall 2008). The matrix is then used to calculate net divergence of each taxon from all other taxa as the sum of the individual distances from the taxon (Hall 2008). That net divergence is used to create a corrected distance matrix after which the pair of taxa with the lowest corrected distance is selected and used to calculate the distance from each of those taxa to the node that joins them (Hall 2008). A new matrix is ultimately created in which the new node is

40 substituted for those two taxa. This method directly calculates distances to internal nodes

(Hall 2008). The neighbor joining method, along with a method called unweighted pair- group method with arithmetic means (UPGMA), are known as distance-based methods.

Distance-based methods consider the overall genetic distance between all pairs of sequences rather than the actual sequences themselves (Nadin-Davis 2013). Other more complex methods, such as the maximum parsimony (MP) and maximum likelihood (ML) methods, are character-based and use individual substitutions to determine all possible tree constructions supported by the data. These algorithms then select the optimal tree by a comparative process (Nadin-Davis 2013). The maximum parsimony method identifies the optimal tree by selecting the minimal number of evolutionary steps required to explain the data. However, this method does not account for the type of changes that occur (Nadin-Davis 2013). The maximum likelihood method does take into account the different types of changes that can occur and the frequency with which they occur (e.g. transition [purine for purine or pyramidine for pyramidine] vs. transversion [purine for pyramidine]). This method creates all possible tree constructions by applying various models of nucleotide substitution for the given data set and then selects the tree that makes the data most likely by calculating the log-likelihood of the data given the tree

(Hall 2008).

Distance methods are often preferred due to their simplicity, relatively rapid execution and their ability to identify groups or clades almost as efficiently as other more complex methods (Nadin-Davis 2013). In general, the association of an isolate within a clade, rather than its precise position within the clade, provides enough information to identify the strain or variant responsible for a given case. Distance methods are sufficient

41 to produce this information (Nadin-Davis 2013). However, more complex methods are necessary for analyzing data for the purpose of exploring mechanisms of mutation and evolution (Nadin-Davis 2013). One method that allows for exploration of the robustness of phylogenetic predictions is the use of nonparametric bootstrap analysis. This analysis is an evaluation of the tree reliability. Most phylogeny software can incorporate this statistical method into their analyses and are encouraged to do so (Nadin-Davis 2013). In nonparametric bootstrap analysis, the nucleotide sequence data are randomly resampled with replacement thus generating pseudoreplicates of the original data. The number of replicates is manually set and is usually between 100 and 1,000 (Nadin-Davis 2013). The proportion of times that each clade occurs within all trees is calculated and this value is considered as a measure of support for that grouping. The most likely branching pattern, the consensus tree, is determined from these. In the case of RNA viruses, bootstrap values >90% are generally regarded as providing strong support for a clade while values

>70% are often considered significant (Nadin-Davis 2013).

A valuable tool for tree construction is the use of outgroups. An outgroup is a representative member of a taxon that is known to be less closely related to all other taxa being analyzed than such taxa are to each other. An outgroup is what provides a root for the tree and determines the direction that characters change (Nadin-Davis 2013). Another useful tool is the application of a molecular clock which permits inference of the time- frame that viral lineages evolve. Computer programs can apply a number of evolutionary models with either a strict or relaxed molecular clock to estimate the viral nucleotide substitution rate. Once this rate is determined, the time frame of the emergence of specific lineages can be predicted (Nadin-Davis 2013). It is important to note that most

42 collections of rabies viruses are drawn from passive surveillance which can introduce significant bias. Areas of low human population density, lack of human contact with rabies reservoir species, or an inadequate infrastructure for laboratory confirmation of suspect cases will significantly limit the number of samples available for study (Nadin-

Davis 2013).

Methods Conclusion:

A wide selection of isolates that are characterized antigenically or by other simple genetic typing methods and supplemented with nucleotide sequence determination on representative specimens is the preferred and most cost-effective means of generating comprehensive epidemiological data (Nadin-Davis 2013).

Rabies Virus Phylogeny in Africa

Rabies virus (lyssavirus genotype 1) has been grouped into multiple different lineages over time as more detailed phylogenetic information becomes available.

Previously, the rabies virus was grouped into 11 lineages based on geography: Africa 1a,

Africa 1b, Africa 2, Africa 3, Asia, Arctic, Europe/Middle East, Latin America 1, and

Latin America 2 (Kissi et al. 1995). Afterward, known rabies viruses are divided into seven major lineages which are estimated to have emerged within the last 1,500 years, an estimate that excludes anecdotal rabies cases described in ancient times (Bourhy et al.

2008, Nadin-Davis 2013). These lineages are divided as follows: 1) Cosmopolitan lineage which includes the clades Africa 1 (with sub-clades 1a and 1b), Africa 4, Europe,

Middle East and parts of North America, Mexico and the Caribbean; 2) Africa 2; 3)

Africa 3; 4) Arctic/Arctic-like with clades Arctic-like 1 and Arctic-like-2; 5) Sri Lanka;

6) Asia with clades Asia 1 and Asia 2; 7) American Indigenous including North America,

Latin America and the Caribbean (Nadin-Davis 2013). Currently, known rabies viruses are divided into two major phylogroups, bat-related and dog-related, that are further divided into clades (Trouping et al. 2016). The bat-related phylogroup includes two major clades: 1.) bat RABVs circulating in the Americas; 2.) RAC-SK comprising viruses from American skunks and raccoons (Troupin et al. 2016). The dog-related phylogroup is composed of six major clades including: 1.) Arctic-related (United States, Greenland,

Russia, India, Nepal, Korea, Pakistan, Afghanistan, Iran); 2.) Africa 2 Central and

Western Africa (Guinea, Benin, Cameroon, Central African Republic, Chad, Nigeria,

Burkina-Faso, Niger, Senegal, Ivory Coast, Mauritania); 3.) Africa 3 (South Africa,

Botswana); 4.) Asian (China, Indonesia, Cambodia, Lao, Myanmar, Thailand,

Phillipines, Taiwan); 5.) Indian Subcontinent (India, Nepal, Sri Lanka); 6.)

Cosmopolitan (Algeria, Ethiopia, Gabon, Nigeria, Morocco and Somalia, Central African

Republic, Kenya, Mozambique, Namibia, South Africa and Tanzania, Madagascar,

Egypt, Israel, United States, Brazil, Mexico, Europe, Russia, China, Saudia Arabia,

Oman, United Arab Emerites, Lebenon, Iraq, Turkey, Iran) (Troupin et al. 2016). The

Cosmopolitan clade includes lineages Africa 1 (AF1) and Africa 4 (AF4). Africa 1 can be further subdivided into AF1a including viruses from Algeria, Ethiopia, Gabon, Nigeria,

Morocco and Somalia, AF1b including viruses from Central African Republic, Kenya,

Mozambique, Namibia, South Africa and Tanzania, and the AF1c including viruses from

Madagascar (Troubin et al. 2016). The AF4 subclade comprises viruses from Egypt and

Istael (Troupin et al. 2016).

More recently, the geographical separation of the Africa 1 and 2 clades has been complicated with the more recent identification of these viruses in west African countries

(Nigeria, Gabon, Ghana) where the Africa 2 lineage was thought to be the only lineage in circulation (Hayman et al. 2011, Nadin-Davis 2013).

Phylogenetic Analysis Summary and Applications

Rabies virus variants undergo selection processes in order to colonize new hosts and/or new geographical regions resulting in new clades or biotypes relating to the local fauna (Hayman et al. 2016). Selection allowing for establishment in new host species can take place on a relatively short time scale (years) as a result of frequent spillover events

(A. Velasco-Villa, personal communication, February 1’st, 2017). During a vicariant event, evolutionary divergence occurs when viruses that colonize new geographic regions start evolving independently to their immediate ancestors, giving rise to slightly different geographic lineages (A. Velasco-Villa, personal communication, February 1’st, 2017). As a result, the virus becomes compartmentalized by species and geographical area leading to distinct virus variants that establish sustained transmission networks (Lembo et al.

2007). However, in areas that are particularly species rich, multiple variants of the virus circulate in different host species or multiple host species are able to maintain infection of a single variant independently (Lembo et al. 2007). Lineages can diverge by accumulation of synonymous mutations in a clocklike fashion (Kissi et al. 1995).

Therefore, sequence differences can be used to assess time to most recent common ancestor (Real et al. 2005). The divergence between sequences can be calculated using phylogenetic analysis. Sequences cluster according to similarity in sequence alignment and therefore the degree of divergence can be calculated. Areas in a sequence where amino acids or nucleotide changes occur can also be identified.

In order to be able to identify a new strain, comprehensive data of all existing sequences in the area need to be identified. A greater amount of sequence data from all species in an area provides greater discriminatory power and more reliable results. If a distinct virus variant is discovered in a species, one cannot say that this variant is unique to that species unless variants from other species in the area have been identified. In order to determine an independent cycle of maintenance within a species, isolates from that species must account for the majority of cases in the area of study in the absence of significant input (high numbers of cases) from other species. For example, South Africa has extensive sequence data on existing rabies virus variants from both wildlife and domestic species. A distinct rabies virus variant that was only found to be associated with

C. mesomelas (black-backed jackal) was identified in western Limpopo, South Africa

(Zulu et al. 2009).With the exception of a short period of dog rabies due to an outbreak,

C. mesomelas was found to account for the majority of rabies cases in the area. The researchers were able to conclude that C. mesomelas are capable of maintaining continuous rabies infection cycles independent of domestic dogs under the specific ecological conditions found in the area (low domestic dog population/low species diversity) (Zulu et al. 2009). Due to fluctuations in disease occurrence, conclusions about independent maintenance should be supported by additional data (Haydon et al. 2002) such as the degree of sequence divergence among strains.

If Ethiopian wildlife species sequences were all found to be slightly different, there would be no way to relate them on a temporal or spacial scale or based on host species without a database of existing sequences in the area. Without this information, very little could be learned from new sequences because there would be no means for

46 comparison. Consequently, there would be no mechanism to identify degree of relatedness or where sequence changes occur. The new sequences would have very little value.

Ethiopia as a Hotspot for Emerging Infectious Disease:

Effects of Biodiversity on Infectious Disease

Biodiversity includes genetic diversity within host populations, species diversity within host communities, and diversity among communities, all of which strongly influence the dynamics of infectious disease (Ostfeld and Keesing 2012). Theoretically, diversity can either increase or decrease pathogen transmission and disease risk (Ostfeld and Keesing 2012). Mathematical models and laboratory experiments have demonstrated that a dilution effect, in general described as a decrease in disease risk with increasing diversity, can occur under a wide range of conditions. Field studies of plants, aquatic invertebrates, amphibians, birds and mammals demonstrate that this is true in natural systems as well (Ostfeld and Keesing 2012). In fact, a review of the consequences of biodiversity loss (Cardinale et al. 2012) found that 80% of statistical tests associating biodiversity and disease transmission showed a statistically significant negative relationship (dilution effect), whereas 12% showed a significant positive relationship

(amplification effect), and 8% were not significant (Ostfeld and Keesing 2012). This pattern occurs across ecological systems that vary in type of pathogen, host, ecosystem and transmission mode (Keesing et al. 2010). Ostfeld and Keesing (2012) describe three conditions that must be met in order for a dilution effect to be observed: 1) hosts differ in their quality as hosts for a pathogen and/or its vector; 2) the species most likely to remain when diversity is lost tend to support greater abundance of the pathogen or vector,

47 whereas those most likely to be added as diversity increases tend to be poorer hosts; 3) the species most likely to be added as diversity increases reduce either encounter rates between high-quality hosts and pathogens or abundance of high-quality hosts. The first condition is relatively straightforward however the other two conditions are better explained through examples.

A good example to support the second condition is the case of Lyme disease in the United States. In this case, the white-footed mouse is the most abundant host species, the most competent host for the Lyme bacterium, and the highest-quality host for immature tick vectors (Keesing et al. 2010). The white-footed mouse is also an ecologically resilient species that tends to be present in both species-rich and species poor communities. In contrast, Virginia opossums are poor hosts for the pathogen and can kill the majority of ticks that attempt to feed on them however, they are absent from many low-diversity forest fragments and degraded forests where mice are abundant (Keesing et al. 2010). Therefore, as biodiversity is lost, the host with a strong buffering effect (the opossum) disappears while the host with a strong amplifying effect (the white-footed mouse) remains. The primary hosts for the pathogens that cause West Nile encephalitis, hantavirus pulmonary syndrome and bartonellosis have also been shown to be resilient species that increase in abundance as biodiversity is lost (Keesing et al. 2010). It is thought that traits that make a host resilient to biodiversity loss may also make them susceptible to pathogen infection and transmission. Consequently, the species that have traits permitting persistence in degraded and species-poor ecosystems are also more likely to carry high pathogen and vector burdens. This relationship would explain the frequency with which the link between diversity loss and disease transmission has been

48 observed in nature (Keesing et al. 2010). An example to support the third condition is an experimental study on Schistosomiasis transmission where host snails were placed in tanks at a constant density either alone or with one or two other species of non-host snails and then exposed to schistosomes. In single-species treatments, host snails were 30% more likely to be infected than in multi-species treatments because schistosomes in multi-species treatments often ended up in dead-end hosts. The multi-species trial also resulted in reductions in contact rate between schistosomes and host snails. As a result, increased parasite-host contact rates caused by reduced diversity were sufficient to increase disease transmission (Keesing et al. 2010).

The effects of scale are also important to take into account. The links between biodiversity and human health occur from the microbial level to that of the habitat

(Pongsiri et al. 2009). Pongsiri et al. (2009) provide examples of how biodiversity has influenced disease transmission at multiple scales through multiple mechanisms. At the microbial level, they provide the example of increasing cases of inflammatory diseases, such as asthma and food allergies, as a result of low contact rates with diverse bacterial communities that are necessary for a balanced immune system (Iweala and Nagler 2006).

At the habitat structure level, they cite that changes in plant diversity (largely through habitat alteration, fragmentation, and deforestation) can increase the risk of malaria transmission through effects on mosquito survival, density, and distribution (Pongsiri et al. 2009). For example, raising surface-water availability due to plant removal can create new breeding sites for some Anopheles mosquitoes (Walsh et al. 1993). Additionally, removal of shade vegetation increases exposure to the sun and results in a warmer microclimate that can cause mosquitoes to digest blood meals more quickly leading them

49 to feed and lay eggs more often. This then results in higher rates of vector development and reproduction (Afrane et al. 2006). The example they provide at the predator diversity level is the apparent rise in cases of Schistosomiasis in humans as snail-eating (snails are the intermediate hosts) fish populations decline in Malawi. Overfishing caused a decrease in density of the snail-eating cichlid Trematocranus placodo which in turn decreased predation on the intermediate host, the snail. The increased density of snails results in increased Schistosoma larval production and thus increased cases of Schistosomiasis

(Stauffer et al. 2006). At the host diversity level, they provide the example of West Nile virus where higher avian diversity was found to be associated with lower mosquito infection rates and human disease incidence. In this scenario, it appears that presence of alternative host species divert mosquito blood meals away from more competent hosts

(Keesing et al. 2006).

As previously mentioned, there are some cases in which increasing biodiversity does not result in a dilution effect. For example, if amplifying species disappear as biodiversity declines, then biodiversity loss will generally reduce disease risk. Thus, disease risk is reduced along with decreasing diversity. In general, reducing biodiversity can increase disease transmission when the lost species are either not hosts for the pathogen or are suboptimal ones. Losing these buffer populations results in increased disease transmission (Keesing et al. 2010). Empirical evidence tends to support this scenario as opposed to the former. These scenarios illustrate the importance of understanding both the non-random sequences by which species are lost from communities, and whether the species that tend to occur only in more species-rich communities tend to amplify or buffer pathogen transmission.

(Keesing et al. 2010). Therefore, the relationship between biodiversity and disease transmission is far more complex than density dependent factors. It is also important to note that though ecosystems with high levels of biodiversity may act as a source pool for pathogens, it is not until the diversity is reduced in these ecosystems that these pathogens emerge.

Ethiopia as a Hotspot

Countries located at low latitudes and with low socio-economic conditions and high levels of biodiversity are considered to be hotspots for emerging infectious disease events (EID’s) (Jones et al. 2008). Ethiopia is a country with all three of these qualities.

A predictive model of future EID events based on 335 previous EID events identified

Ethiopia as one of the countries with the highest relative risks for outbreaks caused by zoonotic pathogens originating in wildlife (Jones et al. 2008).

Ethiopia is part of the Eastern Afromontane Biodiversity Hotspot (Critical

Ecosystem Partnership Fund 2011). This hotspot is roughly 1,017,806 km2 and is made up of three ancient blocks of massifs including the Eastern Arc Mountains and Southern

Rift, the Albertine Rift, and the Ethiopian Highlands plus the volcanic highlands of

Kenya and Tanzania (Critical Ecosystem Partnership Fund 2011). Only 10.5% (106,870 square kilometers) of the original vegetation in this hotspot remains intact with about

15% of the total area (154,132 square kilometers) under some level of official protection

(Critical Ecosystem Partnership Fund 2011).This hotspot is known to have globally significant levels of diversity and endemism in addition to providing tens of millions of people with freshwater and other ecosystem services that are essential to their survival.

The northwestern Ethiopian Highlands alone supply two-thirds of the water of the Nile

51 proper during the June-September rains. More than 30 of the nearly 200 mammals found in the Ethiopian Highlands are found nowhere else including three rodent genera

(Megadendromus, Muriculus and Nilopegamys), one primate genera (Theropithecus) containing the gelada, and one canine genera containing the Ethiopian wolf (Canis)

(Critical Ecosystem Partnership Fund 2011). Despite its wealth in natural resources however, the region is characterized by intense and pervasive poverty (Critical

Ecosystem Partnership Fund 2011).

As part of the hotspot profiling process, all existing ecosystem-related documents were reviewed and key threats, their root causes, as well as barriers to effective conservation within the hotspot boundary were identified through various workshops

(Critical Ecosystem Partnership Fund 2011). Threats were ranked by national workshop participants in order of importance in their country then reviewed by the two regional workshops. The main biodiversity threats identified for the hotspot included habitat destruction and fragmentation due to agricultural development, overexploitation of biological resources (particularly logging and non-timber forest products) and various forms of human intrusion and disturbance and other modifications to natural systems

(such as fire and construction of dams and roads)(Critical Ecosystem Partnership Fund

2011). Invasive species and climate change were identified as increasingly significant threats along with urban spread, mining and other industrial and transport developments

(Critical Ecosystem Partnership Fund 2011). Ethiopia ranked agriculture and aquaculture, biological resources use, human intrusions and disturbance, natural system modifications, invasive and other problematic species and genera, climate change and severe weather as their most sever threats (Critical Ecosystem Partnership Fund 2011).

The Ethiopian Highlands have experienced considerable conversion of natural habitat to agriculture. During the last decade, the population of Ethiopia has increased more than tenfold with around 80% living in the highlands (Critical Ecosystem

Partnership Fund 2011). Much of the original Afromontane forest vegetation now exists only as small remnants, largely restricted to churchyards and other sacred groves surrounded by cropland (Critical Ecosystem Partnership Fund 2011). The greatest loss of native forest in the Eastern Afromontane Biodiversity Hotspot has occurred in the

Ethiopian Highlands where forest cover is now estimated at less than 4 percent of the original forest extent of Ethiopia (Critical Ecosystem Partnership Fund 2011). Only about

11 percent of the country’s land is now forested (Critical Ecosystem Partnership Fund

2011). Deforestation, overgrazing and other poor farming practices along with heavy dependence on dung for fuel (because most wood sources have been removed) are the main drivers of land degradation in Ethiopia (Critical Ecosystem Partnership Fund 2011).

Overgrazing has also led to an increasing abundance of unpalatable or poisonous species and enhanced competition between livestock and wildlife (Critical Ecosystem Partnership

Fund 2011). Overall, 85% of the land is classified as moderately to very severely degraded (Critical Ecosystem Partnership Fund 2011). According to Hanlon and Childs et al. (2013), potential emerging hotspots that may be identified based on land-use change and biodiversity patterns [such as Ethiopia] should be targeted for surveillance of endemic rabies virus variants, and other viruses, that may have the potential to jump host species.

Basics of Rabies in Ethiopia:

Ethiopia has long been among the most rabies-affected countries in the world with a national annual incidence rate of 12/100,000 population rabies exposures and

1.6/100,000 population rabies deaths (Deressa et al. 2013). Estimates of the global burden of rabies showed that some 2,700 humans die of rabies in Ethiopia every year making it the country with the second highest annual number of human rabies deaths in Africa

(Hampson et al. 2015, Coetzer et al. 2016). In 2001, the WHO issued a resolution for the complete replacement of nerve tissue vaccines by 2006 with cell-culture rabies vaccines

(Hurisa et al. 2013). However, sheep brain derived Fermi type rabies vaccine is still being manufactured and utilized for the majority of exposed patients in Ethiopia (Hurisa et al.

2013). A high percentage of adverse reactions to this vaccine introduces an additional health burden to the burden directly resulting from rabies. The proportion of neuroparalytic complications among persons receiving brain tissue vaccines have been estimated at between 0.3 to 0.8 adverse reactions per 1,000 vaccines (World Health

Organization 2005b).

Reta et al. (2014) states that in Ethiopia, rabies remains to be one of the most feared infectious diseases. In a zoonotic disease prioritization workshop held as

Ethiopia’s first step in engagement in the U.S. CDC Global Health Security Agenda, rabies was identified as the number one priority disease followed by anthrax, brucellosis, leptospirosis, and echinococcosis (Pieracci et al. 2016). This was established after thorough review and classification of 43 zoonotic diseases in Ethiopia evaluated on strict criteria. A community-based participatory study of 196 participants carried out in both the highland and lowland areas of the Oromia region (area covering roughly 250km) of

Ethiopia over in 2009 found that both high and lowland areas rated rabies as the zoonosis of greatest risk to public health (Okell et al. 2013).

Many Knowledge, Attitudes and Practices (KAP) assessments about public understanding of rabies have been conducted throughout Ethiopia. A cross-sectional study in the Gondar Zuria District (total human population of 222, 377; 112,248 male and

110,129 female; 90% rural) in northwestern Ethiopia interviewed 400 heads of households in 2013 (Digafe et al. 2015). Results showed that the need for immediate treatment after exposure was mentioned by less than half (47.4 %) of the respondents and only 38.8% of the respondents considered modern medicine as appropriate treatment after exposure to rabid animals (Digafe et al. 2015). Eighty-seven percent reported having encountered rabid animals at least once in their lifetime and nearly half of the respondents (42%) had experienced a dog bite. However, following the dog bites, only

30.7% reported having practiced washing of the wounds with water as first aid (Digafe et al. 2015). Another cross-sectional study carried out in Jimma Town (the capital of Jimma

Zone in Oromia National Region in southwestern Ethiopia; majority of population rural) between July 2012 and March 2013 interviewed 384 bite victims, the majority of which were dog bites, and found that almost all participants (99%) were aware that rabies was transmitted by the bite or lick of a rabid dog however only 20.1% identified “germs” as the cause of disease (Kabeta et al. 2015). Participants recognized domestic dogs as the primary source of rabies and identified a range of appropriate preventive measures including avoidance of bites and the need to confine dogs (Kabeta et al. 2015). However, few of the dog owners interviewed actually confined their dogs and knowledge of first aid following a suspect rabid bite was inadequate in the majority of participants with only

7% having applied first aid after their exposure (Kabeta et al. 2015). Belief in treatment by traditional healers was common among the participants with 75.0% reporting to believe that traditional healers could cure rabies (Kabeta et al. 2015).

A similar cross-sectional study that took place in Addis Ababa, the urban center of the country, found results consistent with those from rural areas (Ali et al. 2014). The researchers in this study interviewed 1,240 households during the months of January and

February of 2011 and found that 83% of the respondents indicated that they had previously heard about rabies while 75.5% knew that rabies could be transmitted through an animal bite (Ali et al. 2014). Interestingly, only 30.97% of respondents recognized rabies as a fatal disease (Ali et al. 2014). This study also found that the majority of the respondents knew that rabies can be prevented in animals through regular vaccination against the disease (46.6%) however only 28.7% recognized the availability of rabies preventive measures in humans (Alit et al. 2014). Of those few that did know about preventive measures in humans, 85.7% correctly answered that both seeking immediate medical help and taking post exposure treatment were effective preventive measures (Ali et al. 2014). Reliance on traditional medicine was again evident with 58.3% of respondents reporting to have had strong beliefs in traditional medicine for rabies prevention and treatment (Ali et al. 2014). In summary, these KAP assessments in both urban and rural areas show that though people seem to know what rabies is and that it is transmitted through the bite of an animal, few know how to treat bite wounds and take other preventive measures while a significant number of people still rely on traditional medicine instead of seeking proper post-exposure prophylaxis.

Most studies of rabies in domestic animals have taken place in and around Addis

Ababa however they all seem to show consistently high incidence cases. A study examining 1,936 rabies positive samples submitted to the public health laboratory from the years 2003-2009 identified rabies in 1,724 dogs, 116 cats, 37 cattle, 13 horses, 19 donkeys and 13 sheep and goats (Ali et al. 2011). A prospective follow up study of suspected and exposure cases of rabies from April 2009 to March 2010 in the human and domestic animal populations of Addis Ababa found rabies incidence of 2.33 cases per

100,000 in humans, 412.83 cases per 100,000 in dogs, 19.89 cases per 100,000 in cattle,

67.68 cases per 100,000 in equines, and 14.45 cases per 100,000 in goats (Jemberu et al.

2013). Yet another study that took place in and around Addis Ababa using retrospective data obtained during 2008 to 2011 examined a total of 935 brain samples from different species of animals of which 77.6% (n = 726) of them tested positive (Reta et al. 2014).

The proportion of rabid dogs that were positive for the virus during this time period was

87.2% followed by cats (Reta et al. 2014).

Though wildlife in Ethiopia have been documented with rabies (Johnson et al.

2010, Deressa 2011, Deressa 2012), little is known about the epidemiology of rabies in wildlife or the likelihood and frequency of spill-over to humans and domestic animals.

Furtheremore, it has been extensively documented that long-term dog-maintained rabies epizootics may favor the establishment of dog-derived rabies virus variants in terrestrial carnivore populations augmenting rabies exposure sources for humans and domestic animals (Badrane et al. 2001, Bourhy et al. 2008, Velasco-Villa et al. 2008). This gap in knowledge is largely due to the fact that Ethiopia currently lacks wildlife disease surveillance. What is known only comes from passive surveillance and is particularly

57 disturbing: among wildlife and domestic species in and around Addis Ababa (excluding cats and dogs), roughly 60% of animals tested were positive for rabies (Deressa et al.

2010). Because the wildlife population is low in and around Addis Ababa compared to other parts of the country, people who traditionally link rabies with dogs rarely submit brain samples from other animal species including wildlife. This underestimates the potential role of wildlife in the transmission cycle of rabies (Reta et al. 2014).

Additionally, rabies transmitted by wildlife species could be a future challenge in the country when rabies transmitted by domestic carnivores is controlled (Reta et al. 2014).

Despite vaccination campaigns and population control efforts for domestic dogs in targeted areas, such as the Bale Mountains (Randall et al. 2006), rabies remains endemic in Ethiopia, suggesting wildlife may play a role in rabies persistence or that there is re- introduction of dog-maintained rabies virus variants from neighboring regions.

Modeling Basics and Obtaining Modeling Data from Wildlife:

The Model-Based Approach

Mechanistic models can be very valuable when working with complex biological systems by providing a framework for a comprehensive and effective approach. Many researchers fail to apply these models for study design and data integration during the early phases of field studies which can limit the power of data analysis and applicability at later stages (Restif et al. 2012). Infectious diseases in wildlife are particularly challenging ecological systems because their dynamics are determined by processes operating at multiple scales (e.g. within host, between host, across landscapes, from wildlife to humans). Restif et al. 2007 propose an approach termed “model guided fieldwork “ (MGF) that attempts to focus field studies on the most important structures

58 and drivers of dynamics before data is collected. This framework is not meant as a rigid set of rules but more as a guideline for multidisciplinary integration. They propose a five step process that is not to be considered linear but rather more cyclic. The five steps are described below:

1.) Model Generation- Integration of the evidence-based, qualitative and quantitative descriptions of the processes being addressed into a formal mathematical model that attempts to describe the dynamics of observed variables. In disease ecology, this includes numbers of individuals in different disease categories. Restif et al. 2007 suggest that instead of trying to falsify a single hypothesis, it is often best to formulate a comprehensive set of biologically plausible alternative hypotheses and assess their relative merits to explain available data. In this step, researchers are encouraged to be extremely specific in detailing their questions of interest and underlying assumptions to ensure that the data collected will be appropriate for the subsequent analysis.

2.) Model Exploration- Once a draft model has been constructed, its dynamics must be explored over a wide range of parameter values and alternative assumptions using mathematical analysis and numerical simulations. One way to do this is to use sensitivity analysis which is an essential process that helps focus data collection effort on the most important parameters by determining how changes in parameters affect model output.

3.) Study Design- Ensure that data collection is designed so that information on key parameters is collected. Once this is complete the data must then be collected.

4.) Model Fitting- When using alternative hypotheses that have been incorporated into different models, it is necessary to determine which hypothesis provides the ‘best fit’ of the model to the data. One can use various statistical methods however Restif et al.

(2007) seem to favor the Bayesian framework which has all model parameters follow probability distributions rather than being treated as fixed quantities. As a result, measures of parameter uncertainty can be generated.

5.) Model Validation- The first step is the assessment of the ‘goodness of fit’ of the model(s). Statistical tests can be used to assess whether the remaining differences (or residuals) between the fitted model and the actual data can be attributed to random noise.

The second step involves confronting the predictions of the model with an independent set of data, e.g. data not used in the fitting procedure. If the model fails this step of validation, alternative models must be considered. The third component is an assessment of the importance of the model parameters. One can use an information criterion framework that penalizes the explanatory power of models by their complexity (number of parameters). Alternatively one can use sensitivity analysis on a single validated model to determine the relative importance of different processes incorporated in that single model. Validation can result in modifications of the model as well as additional experiments.

Modeling Basics

Understanding disease dynamics across hosts is an essential first step to understanding the conditions under which new diseases can emerge from wildlife reservoirs. The most basic structure for starting a disease model is known as the

Susceptible-Infected-Recovered, or SIR, model. In this model, the susceptibles are individuals that have never been infected with the pathogen, the infected are individuals who are currently infected with the pathogen and are capable of infecting others, and

60 recovered individuals are those that have mounted an immune response, cleared the pathogen, and acquired immunity. There are many variations of this model based on different disease states. For example, one could add an “exposed” state in-between the susceptible and infected states and even a “latent” state after that. The goal is to provide the most accurate representation of the disease dynamics produced by the pathogen in question.

In order to create these models, one must rely on a set of parameters. The basic parameters include the infection parameter (I) which represents the number of infected individuals in the population being examined, the recovery rate (γ) or the per capita rate at which infected individuals recover, the average infectious period or (1/ γ ), the force of infection (λ) which can be defined as the per capita rate at which susceptible individuals are infected and the transmission coefficient (β) which represents the combined probability of a contact with the chance of disease transmission over that contact. These parameters are combined to represent the disease states in the following equations: dS/dt=−λS [the change in number of susceptible individuals per unit time (always decreasing as long as disease is maintained in a population)] dI/dt=λS−γI (the change in number of infected individuals per unit time) dR/dt=γI (the change in the number of recovered individuals per unit time)

The force of infection drives the transmission dynamics. This parameter is made up of the transmission coefficient (β) and the number of infected individuals. The transmission coefficient can be further divided into a number of biologically relevant variables including contact rates among individuals, prevalence of susceptible individuals, and the

61 chance of a contact being between a susceptible and infectious individual among others

(Garabed and Pomeroy 2015).

There are two common techniques used to estimate β within a population. The first method, known as the secondary attack rate (SAR), focuses on the fate of a single infected index case that comes into contact with many susceptible individuals in the population (Real and Biek 2007). The second method is known as the binomial model of transmission probability. This model tracks one uninfected but susceptible host as it comes into contact with many infectious hosts. Both methods have been commonly used in human disease epidemiology but have rarely been used in assessing wildlife disease dynamics (Real and Biek 2007). However, these methods should be extended to wildlife disease dynamics.

The secondary attack rate is defined as the ratio of the number of hosts exposed that develop disease relative to the total number of susceptible exposed hosts (Real and

Biek 2007):

SAR= total secondary cases/ total susceptible exposed

In order to understand SAR, one must understand how primary and secondary host are defined according to the time course of infection. A primary host is assumed to be characterized as having a maximum infectious period (I) which is the maximum amount time that individuals within the host population can remain infectious, a minimum incubation period (E1) or the minimum time required before symptoms appear, and a maximum incubation period (E2) or maximum time period before which symptoms will appear (Real and Biek 2007).

Figure 4. Time Course of Infection.

(Image taken from Real and Biek 2007)

Under these assumptions, the only individuals that could have become infected by the primary host and are true secondary cases are those that fall within the time interval between the minimum incubation period (E1) as the lower bound and the maximum infectious period + the maximum incubation period (I+E2) as the upper bound (Real and

Biek 2007) (Figure 4). Any individuals appearing symptomatic outside of this time interval are not considered to be secondary cases. An exposure must be defined by the researcher based off of known disease parameters. For example, Kendrick and Eldering

1939 calculated the SAR for pertussis by defining an exposure as any contact with the primary case lasting for at least 30 min during the infectious period. One must be especially careful defining terms for calculating the SAR when dealing with diseases that have a latency period. The latency period must always be accounted for.

In order to properly account for latency, one must understand the difference between the time course of infectiousness (the time interval over which infected individuals are capable of transmitting the pathogen to susceptible individuals) and the time course of disease (expression of symptoms associated with infection) (Real and Biek

2007). Latency is a component of the time course of infectiousness. Latency occurs after the initial infection when the pathogen can be resident in the host but not able to be transmitted to other hosts (Real and Biek 2007). This is followed by an infectious period and then a non-infectious period such as recovery or death. The time course for disease differs from infectiousness in that upon the onset of infection, the host moves into an incubation period where disease symptoms do not occur. When symptoms appear, the host moves into the symptomatic period that lasts until the symptoms disappear and the host recovers or dies. The initiation and duration of these periods may not correspond

(Real and Biek 2007). For example, for some pathogens such as HIV, the latency period can be shorter than the incubation period in which case hosts are infectious before symptoms appear. Conversely, there are diseases such as malaria where the latent period can be longer than the incubation period (Real and Biek 2007). The incubation period for malaria in humans is 14 days but humans are not able to infect mosquitos until 10 days after onset of symptoms (Halloran 1998). Therefore, in order to properly identify a secondary infection using the minimum incubation period as the lower bound and the maximum infectious period + the maximum incubation period as the upper bound, one needs to be able to distinguish and identify these periods correctly.

The binomial model of transmission is defined by probability. If the probability of disease transmission during a single contact with an infected host is represented as p, then

64 the probability of escaping infection following a contact with an infected host is q = (1- p). If a susceptible individual makes n contacts with one or multiple infected hosts, then the probability of becoming infected after n contacts is 1-qn = 1-(1-p)n (Real and Biek

2007). This is the description for the binomial distribution. The maximum likelihood estimate for p is expressed as:

푝̂ = number of individuals who become infected/total number of contacts with infectives

The SAR and binomial model of transmission will be identical when every susceptible individual has contact with one, and only one, infectious host (Real and Biek 2007).

Disease models can represent transmission as being either density-dependent or frequency-dependent. In density-dependent models, the number of contacts that occur is proportional to population density. Transmission rates will then increase with increasing population density (Garabed and Pomeroy 2015). The force of infection in a density- dependent model can be calculated as the product of the transmission coefficient and the number of infected individuals (λ=βI) (Garabed and Pomeroy 2015). Density-dependent transmission can be expressed as (dI/dt= βSI) where dI/dt is the rate of change of infectious individuals over time (Garabed and Pomeroy 2015). In frequency-dependent models, the number of contacts is assumed to be a fixed (Garabed and Pomeroy 2015).

The proportion of infected individuals in a population is used for calculation of the force of infection instead of the number of infected individuals. The number of contacts that occur is not proportional to population density and transmission rates will not change with population density (Garabed and Pomeroy 2015). The force of infection for a frequency-dependent model can be calculated by multiplying the transmission coefficient by the proportion of infected individuals (λ=β*[I/N]). Frequency-dependent transmission

65 can be expressed as (dI/dt= βSI/N) where N is the total number of individuals in a population(Garabed and Pomeroy 2015).

A model can also be either deterministic or stochastic. Deterministic models are more basic models that produce outputs that are identical given the same initial input values (Garabed and Pomeroy 2015). Each variable and parameter is stated. Stochastic models incorporate elements of variability or chance so that repeated runs of the model can produce different results. These models attempt to incorporate the effects of changing demographic and environmental variables (Garabed and Pomeroy 2015).

When creating disease models, one of the primary goals is to be able to determine a key parameter known as the basic reproductive number, or R0, which captures many of the primary features of disease dynamics (Real and Biek 2007). The basic reproductive number is the average number of secondary infections caused by a primary infection when introduced to a completely susceptible host population. The basics reproductive number can be determined mathematically, statistically, or through contact-tracing. It depends on the rate of contact between susceptible individuals and the index case per unit time (푐̅), the probability of successful transmission per contact between a susceptible and infected individual (τ), and the duration of time that the index case is infectious (1/γ)

(Real and Biek 2007). It can be described mathematically as:

R0 = (푐̅ * τ)/(γ)

This formula can also be expressed in terms of density and frequency dependence. The 푐̅ and τ terms can be combined into β so that a density-dependent R0 would be expressed as

(β * N)/(γ) while a frequency-dependent R0 would be expressed simple as (β/γ) (Garabed and Pomeroy 2015). There are also alternative ways to calculate R0 without knowing

66 these components. For example, R0 can be measured as the average per capita rate of increase in infectious individuals when a pathogen emerges into a new population with no previous exposure (Real and Biek 2007).

The basic reproductive number is a threshold for invasion. The disease will increase if R0 > 1 and will fade from the host population into extinction if R0 < 1. If R0 =

1, then every infectious individual replaces itself with one new infectious individual and the disease prevalence in the population will be stable meaning that the disease is

“endemic.” There is a minimum proportion of susceptible individuals for an outbreak to occur which can be expressed as ST =(1/R0) (Garabed and Pomeroy 2015). To prevent an outbreak, one can try to force the fraction of susceptibles below ST by vaccinating a critical proportion of the population. This can be expressed as pc = 1−(1/R0) (Garabed and

Pomeroy 2015).

The basic reproductive number does have several limitations. It is not a fixed property of a pathogen but rather depends on the population of hosts governed by specific contact patterns, durations of infectiousness, and transmission probabilities (Real and

Biek 2007). As a result, one could have very different biological transmission processes that generate identical basic reproduction numbers. This can make comparisons of R0 across diseases very difficult. What R0 does captures is the ability of a disease causing agent to generate an epidemic given some, often unknown or not assessed, transmission process (Real and Biek 2007). For the purposes of predicting disease emergence, it would be ideal to know the values of the underlying components of transmission that produce the overall pattern of R0 and how these components might change under alterations in environmental conditions (Real and Biek 2007). As a result, it is important to develop

67 methods for the direct assessment of the components of R0 as a goal toward increasing capacity to predict disease emergence.

Infectious disease modeling is a diverse field that serves as a valuable epidemiological and ecological tool. Once a basic model can be developed for transmission of a pathogen, other elements are built in to the model to account for all potential factors. This is where heterogeneity and stochasticity are introduced. For example, prevalence and population size are not stable systems. In order to account for changes in population structure, one can introduce population dynamics into the model by adding birth rates, death rates, disease-induced mortality, and other elements that influence population structure. Additionally, competitive and predatory interaction between species must be taken into account. The behavior and distribution of a species has a large impact on whether mixing between species is heterogeneous or homogeneous.

A diversity of disease states must also be considered when adding on to the model. Some disease states to consider include maternally immune, recovered/immune, infected/infectious, susceptible, exposed/latent, vaccinated, treated, quarantined/isolated, dead/culled, and carrier/persistently infected (Garabed and Pomeroy 2015). An equation should be created representing each disease state. More detailed information about how to account for these factors using disease models can be found in Allen et al. 2012.

Environmental heterogeneity is an important factor that must be considered for all pathogen transmission models. Geographical factors, natural weather cycles, and anthropogenic factors can all significantly influence transmission dynamics. There are 3 basic ways to incorporate environmental heterogeneity. One way is to simply change an existing parameter to match changes in the environment (Garabed and Pomeroy 2015).

For example, one may increase the β value to account for increased transmission due to temperature. A second method is to create two separate but interacting models (Garabed and Pomeroy 2015).This works well for geographical separation and is especially important when considering the effects of one population on another. The Ross-

Macdonald models of transmission between mosquitos and humans are prime examples of separate but interacting models (Mandall et al. 2011, Smith et al. 2012). A third approach is to incorporate seasonal forcing. The basic component of seasonal forcing is that it allows the β value to vary over time (Garabed and Pomeroy 2015).. The basic formula for seasonal forcing is as follows:

β(t) = β0(1 + β1cos(wt))

β(t) = β value at given time point

β0 = baseline or average transmission rate

w = period of the forcing

β1 = amplitude of seasonality

For example, increased transmission of malaria tends to occur during rainy seasons. In this case, the β value would increase until it reaches peak transmission levels at β1 during a 3 months rainy season (w). Not only is there environmental heterogeneity to consider, but there is also host heterogeneity. For example, within a population there may be a high risk group and a low risk group. In order to account for this, one can again use separate but interacting models.

If parameters are unknown then they must be estimated. Usually, a biologically plausible number is selected to represent the unknown parameter. Once estimates have been made, one way to evaluate the estimated parameter value, such as β, is to use

69 likelihood. In general, likelihood is a way to measure how well the model fits the data

(Garabed and Pomeroy 2015). The likelihood function determines how probable our data are for a given model. Specifically, it examines the relationship between the data and the parameter estimates as shown in the following function (Garabed and Pomeroy 2015):

L(θ|x) = p(x|θ)

L(θ|x) = the likelihood function

θ= paramater estimates x = the data p(x|θ) = the probability of the data given the parameters

The likelihood for a range of parameter values can be compared to see which value gives the best-fit of the model to the data (maximum likelihood value) (Garabed and Pomeroy

2015). Another way to evaluate parameters is to use sensitivity analysis (Garabed and

Pomeroy 2015). When using sensitivity analysis, a range of biologically plausible estimates are plugged into the model to determine whether or not changes in the parameter values have a significant impact on model outcomes (Garabed and Pomeroy

2015). If the effects of changing the parameter do not significantly impact model outcomes, then the mean of the range of estimates can be used. If changes in parameter values do have a significant impact on model outcomes, then there is significant uncertainty and more information on the parameter must be obtained.

When multiple parameters are unknown, one must consider how changes in one parameter will affect other unknown parameters. The range of estimates for each unknown parameter must first be broken into a reasonable number of equal divisions such as high, middle, and low (Garabed and Pomeroy 2015). Afterward, each

70 combination of plausible parameters should be tested. Creating a Latin Square is a simple way to determine which combinations must be tested (Garabed and Pomeroy 2015). For example, if both β and γ are unknown, then one could create the Latin Square in Table 1:

Table 1. Latin Square

Γ High Middle Low Β High High/High High/Middle High/Low Middle Middle/High Middle/Middle Middle/Low Low Low/High Low/Middle Low/Low

Once all combinations have been established for the different parameter estimates, a sensitivity analysis can be run for each combination in order to determine the effects of the changes on model outcomes. If the effects appear to be minimal, then using a mean value for each unknown parameter could be useful. Otherwise more information needs to be acquired to produce better parameter estimates.

Once a final model is created, it can be compared to simpler versions of the model to determine which is the best-fit model. One way to select a best-fit model is to use the likelihood ratio test (Garabed and Pomeroy 2015).This test selects one of two nested

(collapsible) models which best fit the data using the following formula:

Likelihood ratio test = -2ln (L0/LA

L0 = likelihood of simplest model

LA = likelihood of more complex model

Once the best-fit model has been selected, it must be validated before drawing any conclusions. Traditionally this involves an examination of parameter sensitivity, internal consistency and a comparison with external data (Garabed and Pomeroy 2015).

Evaluation of parameter sensitivity allows uncertainty to be quantified by determining how much a parameter would have to be changed in order to change the results (Garabed and Pomeroy 2015). Internal consistency simply determines whether or not the model is able to successfully reproduce the data that was used to create it (Garabed and Pomeroy

2015). Lastly, comparisons with external data produced by new or different outbreaks events of the same disease must be made (Garabed and Pomeroy 2015). If the model fits the new data then the model is considered successful. If the model does not fit the new data however, then the model must be altered to account for the new data. A successful model is extremely valuable and can serve many purposes. One of these purposes is to test the effects of different intervention strategies to determine the most cost-effective control measures. For example, if a model shows that transmission of a pathogen can only be pushed below its threshold (R0) if 98% of the population is vaccinated, then vaccination might not be the best method to use. Alternative methods should be considered because vaccination of 98% of any population is very difficult to obtain in addition to being very costly. This is just one of many ways that disease models make disease elimination possible.

Allometrics as a Means to Simplify Parameterization

Modeling disease transmission in any multi-host system can be fraught by the large number of parameters that need to be estimated. One way to simplify this problem is to scale the model parameters as allometric functions of the host species by body size

(Dobson 2004). Population density, birth rates, and death rates tend to scale allometrically with body size for most vertebrate species. Because the majority of parameters that make up SIR pathogens are functions of host body size, transmission rate can more easily be estimated by plugging the scaled parameters into the model and finding the threshold value that just allows the pathogen to establish (R0 = 1) (Dobson

2004). As a result, the interval between disease outbreaks will scale with host body size.

This makes sense because host birth rate is the primary determinant of the rate at which new susceptibles are created (Dobson 2004). Additionally, the R0 value will increase with host body size if transmission is density- dependent because the number of encounters per unit time decreases with body size as a result of the lower population density (De Leo and Dobson 1996). Consequently, higher transmission rates are required for the disease to spread. When transmission is frequency-dependent, the probability of successfully transmitting the disease is not dependent on density but rather on the proportion of time spent in the infective class (De Leo and Dobson 1996). Assuming that larger individuals have a longer infectious period, R0 would decrease with host body size in a frequency- dependent system (De Leo and Dobson 1996). This reflects the dependence of transmission efficiency on population density in density dependent systems and the dependence of transmission efficiency on the life-span of the infected hosts in frequency- dependent systems (De Leo and Dobson 1996).

Cable et al. 2007 found that key epidemiological components of a pathogen’s interaction with its host show features of allometric scaling as well. Through running disease models using data from an extensive literature search on pseudorabies virus, rabies virus, Anthrax bacteria, transmissible spongiform encephalopathy and West Nile

Virus, they found that the pace of host-pathogen interactions, or pathogenesis, is set by rate of host metabolism (Cable et al. 2007). The host metabolic rate influences pathogenesis by: 1) constraining the rate of growth of pathogens that rely on host metabolic machinery and 2) influencing the rate of the immune response of the host

(Cable et al. 2007). Cellular-mediated immunity has been shown to scale with body size and associated life history traits (Cable et al. 2007). Therefore, host metabolic rate influences the rate of pathogenesis since the ability of a pathogen to invade and replicate within a host may be driven by the physiological rates of the host (Cable et al. 2007). For example, Cable et al. (2007) found that both incubation periods as well as time to death of a host infected with a given pathogen seem to scale with host body size. Using this information, one can then modify the pathogen or host parameters to examine ways in which biological changes in susceptibility, virulence, or transmission cause persistence of the pathogen (Dobson 2004).

Who Acquires Infection from Whom (WAIFW) Matrix Model

The “Who Acquires Infection From Whom” matrix (WAIFW) is a valuable tool when examining heterogeneity in multiple host species systems. This technique assumes that the host populations can be divided into discrete classes based on disease state and other distinguishing population parameters (eg. species 1, species 2, species 3) and that rates of transmission from one class of host to any other class of host can be compared

(Dobson and Foufopoulos 2001). The host classes are then arranged into a “next generation matrix” in which each element consists of the product of the rate of transmission from one host class to another and the duration of time for which an individual in in a given class is infectious.

In order to begin, the focus must be on simple within-and between-host species transmission. It is acknowledged that between-host species transmission will be influenced by three different components: 1.)the spatial distribution of each host species;

2.) the within-and between-species contact rates; 3.) the physiological components that determine both susceptibility when exposed to infection and the rate at which infective individuals are produced (Dobson and Foufopoulos 2001). All other heterogeneities are ignored at this point. In order to represent these components into the matrix, the first step is to assume that all of the components of transmission from individuals of different host species can be captured by a single transmission parameter β (Dobson and Foufopoulos

2001) (Table 2).

Table 2 Who Acquires Infection from Whom Matrix

S I R

Species 1 β1,1 β1,2 β1,3 Species 2 β2,1 β2,2 β2,3 Species 3 β3,1 β3,2 β3,3

For each element βi,j in the matrix, the subscript i represents the species and the subscript j represents the disease category (1= Susceptible; 2=Infected; 3= Recovered/Removed).

Here, all β’s are the same.

The second step is to allow within-stage transmission (the diagonal of the transmission matrix) to vary by multiplying β by a scaling factor ƙ (Dobson and

Foufopoulos 2001) (Table 3).

Table 3.Who Acquired Infection From Whom Matrix with Scaling Factor for

Within-Stage Transmission

S I R

Species 1 ƙ β1,1 β1,2 β1,3 Species 2 β2,1 ƙ β2,2 β2,3 Species 3 β3,1 β3,2 ƙ β3,3

The third step is to allow the β value to vary for each element. This is the same matrix as in Table 2 except each β represents a different value instead of the same one. This allows the transmission term to vary with the interactions within and between stages. The transmission matrix is assumed to be symmetrical: βi,j = βj,i. This captures the assumption that the probability of two hosts in different stages infecting each other is equal (Dobson and Foufopoulos 2001).

The fourth step is to allow βi,j and βj,I to vary. Here, the force of infection for each species is calculated by incorporating the variability in the transmission coefficients between two different species (Dobson and Foufopoulos 2001). For example, dogs may exert a greater force on hyenas than hyenas do on dogs when transmitting the rabies virus. This value can then be incorporated into an epidemic transition matrix for a given species. In order to calculate the force of infection for a class assuming density dependence, the following formula may be used:

φi(n(t)) = 1 – exp (-∑ 훽ijnj2(t))

φ = force of infection

(n(t))= number of individuals in species i at time step t

βij = probability per time step of disease transmission between a susceptible individual in demographic class i and an infected individual in demographic class j, where the time step is 1 day nj2 = number of infected individuals in species j t= time step

This formula includes all the possible transmission events with infected cases in all demographic classes (Dobson and Foufopoulos 2001).

The epidemic transition matrix for species i would be assembled as in Table 4:

Table 4. Epidemic Transition Matrix

S I R S 1-φi(n(t)) 0 0 I φi(n(t)) 1-γ 0 R 0 Γ 1

SS= probability of a susceptible remaining susceptible IS= probability of infected becoming susceptible RS= probability of recovered becoming susceptible SI = probability of susceptible becoming infected II = probability of infected staying infected RI = probability of recovered becoming infected (if individual cannot become susceptible again then this is equal to zero) SR = probability of a susceptible recovering IR = probability of infected becoming recovered RR = probability of recovered becoming recovered

In order to determine how the species interact with one another, a larger matrix that includes an epidemic transition matrix for each species would then be multiplied by a population vector (Klepac et al. 2009). If, for example, three species were being examined, then the population vector would be as follows: n = (n1,1 n1,2 n1,3 | n2,1 n2,2 n2,3 | n3,1 n3,2 n3,3 )

For each element ni,j, the subscript i represents the species and the subscript j represents the disease category S, I, or R. The result would provide the force of infection exerted by each species (Klepac et al. 2009).

In order to produce an R0 value, each term in the matrix must be multiplied by the average duration of infection for an individual of the species transmitting the pathogen

(Dobson and Foufopoulos 2001). Each term is then the rate of transmission from one species to another multiplied by the duration of time for which an individual of the given species is infectious (Dobson and Foufopoulos 2001). This produces the matrix in figure

Figure 5. Advanced Epidemic Transition Matrix for Calculation of R0

bi = mortality rate of host species i ai = pathogen-induced mortality of host species i μi = recovery rate of an infected individual of host species i pij = determine whether transmission is density dependent or frequency dependent.

Frequency dependent pij = terms are unity multiplied by the relative proportion of interspecific contact.

Density dependent pij = terms correspond to the product of the density of species j and the proportion of total contacts that species j has with species i .

The R0 is then the dominant eigenvalue of the matrix (Dobson and Foufopoulos 2001). In these R0 matrices, the sum of each column provides an index of the relative force of infection exerted on each species, while the sum of each row reflects the relative force of

78 infection exerted by a species (Dobson and Foufopoulos 2001).The species that exert the largest force of infection are likely to be the ones against which control might most effectively be introduced. Those that experience the strongest force of infection may be the ones that receive the most significant impact from the presence of the pathogen.

It is important to note that in the situation of asymmetrical transmission (e.g. transmission occurs from bats to pigs but there is no reciprocal transmission from pigs to bats), all elements in the WAIFW matrix above the leading diagonal will be 0. For all matrices of this type, the dominant eigenvalue is the largest of the diagonal terms

(Dobson 2004). This implies that the multispecies dynamics consist of three almost independent epidemics: 1) the background endemic infection in the reservoir host; 2) the epidemic that may occur in the first emergent host if the conditions occur to allow transmission from the reservoir to the first emergent host; 3) the outbreak that occurs in the second emergent host if transmission occurs from the first emergent host to the second emergent host is allowed to occur (Dobson 2004). Nipah virus would be an example of this situation (Dobson 2004). In this case, the between-species transmission event usually reflects a mix of stochastic and deterministic events such as modification of reservoir habitat. Once between-species transmission has occurred, the dynamics of infection are essentially driven by within-host dynamics (Dobson 2004).

Additional Methods to Consider When Examining Host-Species Heterogeneity in

a Multi-Host System According to Streicker et al. 2014

Significant heterogeneity among host species may result in certain species contributing disproportionately to transmission and becoming “key hosts” (Streicker et al.

2014). Streicker et al. (2014) identified three ways in which a host species may become a

79 key host and describe a mathematical framework to partition the contribution of each process. They summarize host heterogeneity resulting in disproportionate contributions to disease transmission into three types of host species: 1) super-abundant hosts ; 2) super- infected hosts; 3) super-shedder hosts. They create mathematical models to describe each type of host. In general, they state that a host species will show abundance asymmetry if it is more abundant than expected based on the community average (average number of individuals per host species across the whole community). A host species will show infection asymmetry if it is infected more often than expected based on the community average prevalence (the average prevalence of infection across the whole host community regardless of species). Lastly, a host species will show shedding asymmetry if it produces infective stages in each infected host greater than expected based on the community average (the total number of infective stages shed by all infected host individuals in the community divided by the total number of infected hosts regardless of species). The relative contribution of each host species to total transmission within the system is then proportional to the product of that species’ abundance, infection and shedding asymmetries (Streicker et al. 2014). In order for a species to be considered a key host, at least one of the asymmetries must significantly exceed one (Streicker et al. 2014).

The researchers then used empirical data from 11 gastrointestinal parasites in small mammal communities across the eastern United States to explore the efficacy of three hypothetical control strategies for each parasite species consisting of random removal regardless of host species or infection status (random), removal of the host species that contributed the largest proportion of infective stages without respect to infection status

(untargeted), and targeting of only infected individuals of the species that contributed the

80 largest proportion of infective stages (targeted). They found that in nearly all cases, targeted and untargeted removal controlled infection more efficiently than random removal (Streicker et al. 2014). Compared with untargeted control, their results showed that targeting infected hosts substantially reduces the number of removed hosts needed to deplete the infectious pool for super-abundant and super-shedding key hosts, but provides minimal benefits when key hosts are super-infected (Streicker et al. 2014). The methods developed by Streicker et al. (2014) provide a means to quantify these processes and identify key hosts. Using their method can provides a better understand how pathogens are maintained in a multi-host system and where to target disease management strategies.

Network Models for Wildlife According to Craft and Caillaud 2011

Craft and Caillaud (2011) suggest that network models are the best type of model for wildlife disease epidemiology. In these models, individuals, or groups of individuals, are defined as nodes. Connections between the nodes are called edges, and the number of edges from one node to another is known as the degree. In network epidemiology, diseases is spread from node to node following the edges (Craft and Caillaud 2011). The researchers state that these models are best for wildlife populations because all individuals and all potential transmission paths are represented in the network making it possible to identify individuals or edges that play a key role for disease transmission. The degree distribution is able to capture heterogeneity in transmission among hosts allowing disproportionate roles played by highly connected individuals, known as superspreaders

(similar to what was discussed by Streicker et al. 2014), to be identified. Networks also include lists of attributes associated with the nodes or edges that describe between-edge variation in disease transmission or between-host variation in infectiveness or pathogen

81 excretion patterns (Craft and Caillaud 2011). They argue that traditional compartmental or metapopulation models pool the individuals in an epidemiological system into a small number of functional groups within which the disease incidence rate is simply proportional to the number of susceptible and infectious individuals. Within the functional groups, all individuals are assumed to be epidemiologically identical. They state that the strength of network models lies in their ability to take into account internode variations in epidemiological properties such as degree, infectiveness and recovery rate.

This makes them a powerful tool for examining heterogeneous epidemiological systems.

When using network models, one needs to keep in mind that wildlife data is fundamentally different from human data. Wildlife systems differ from human systems in four important ways: 1) they have different underlying population structures; 2) data collection on the network relies on different tools; 3) the existing available epidemiological data is much different; 4) potential control options are much different

(Craft and Caillaud 2011). These differences present many challenges when it comes to data collection. For example, one has to consider factors that they would not generally have to consider when dealing with humans such as ensuring that collection methods do not alter the behavior of the animal. Though there is a great deal of potential for using network models, the methods used to collect the appropriate data from wildlife are still being developed.

Obtaining Modeling Data from Wildlife

The estimation of the components that make up β, the probability of a contact between a susceptible and infected individual and the probability of disease transmission over that contact, is rarely attempted for in natural populations due to the temporal and

82 spatial resolution at which this epidemiological data must be collected (Real and Biek

2007). For example, in human populations, information about contact between individuals after an outbreak event can be obtained through interviews and questionnaires. Though this is not possible for wildlife species, a great deal can be learned from observational studies of behavior. Social species probably offer the best opportunities to quantify contact rates (Real and Biek 2007). Determining rates of contact between social groups as opposed to within social groups presents more of a challenge because such events will occur more rarely. Unfortunately, between-group rates of transmission are of much greater interest to disease dynamics because transmission within a group is unlikely to have much effect on overall disease dynamics compared to the rate at which the disease is introduced to new groups (Real and Biek 2007).

However, increasing the intensity of monitoring on both a spatial and temporal scale can make this data obtainable (Real and Biek 2007). For example, placing more camera traps throughout a greater area over a longer period of time can significantly improve data on between-group contact events.

Contact information for solitary species will be more difficult to determine because contacts will occur less frequently (Real and Biek 2007). However, if opportunities for pathogen transmission are restricted to certain habitat features such as watering holes or scavenging sites, then intensive monitoring of these habitats can provide valuable data (Real and Biek 2007). When it is not possible to obtain the necessary contact data through direct observation, indirect measures may be used. One of the more commonly used methods to indirectly determine contact rates in wildlife involves fitting a known proportion of the population with passive integrated transponder

(PIT) tags (Real and Biek 2007). These tags are inserted into the species of interest while a hidden antenna connected to a data logger is placed in a strategic location, such as at a burrow entrance. The data logger then records each time a marked individual passes the strategic location along with the identity of the individual (Real and Biek 2007). An interaction can then be recorded when two marked individuals pass through the same strategic location within a predetermined amount of time of each other (Sutherland et al.

2005, Real and Biek 2007). Radiotelemetry is useful to determine use of space however the temporal resolution of this data is often insufficient (Real and Biek 2007). The use of proximity loggers can help overcome this obstacle however (Real and Biek 2007). These devices can use fixed receivers and interactive tags to record and correlate the time that individuals spend at common locations as well as interactions with another transmitter

(Ryder et al. 2012). This technique was successfully used to produce data on connectivity of raccoon social networks. The collected data was then applied to models to determine the potential for transmission of the rabies virus (Hirsch et al. 2013). The use of GPS recorders is becoming more common and can be effective when looking at contact between individuals as long as all individuals in a study area have recorders and the recorders are well synced with one another (Craft and Caillaud 2011). A potential problem with approaches involving electronic tags is that they will underestimate the number of encounters unless all individuals in a population are fitted with a tag. As long as the proportion of the population that is tagged is known, it is possible to correct for bias (Real and Biek 2007).

In order to determine whether a contact event resulted in a new infection can be a bit more difficult. Ideally it would be best to have detailed knowledge on the disease

84 status of all individuals in a population through time (Real and Biek 2007). However, this type of data is rarely obtainable. In this case, the most effective strategy to determine the number of contacts that resulted in successful transmission of disease is to combine information on population size and disease prevalence within the population/s of interest with the information about contact rates within or between populations (Real and Biek

2007). In order to obtain information on disease prevalence within a population, one can capture and sample a number of individuals from the population to determine infection status. This information can be extrapolated to the entire population of interest if the size of the population is known or has been estimated. Because trapping and sampling may be too disruptive to natural cycles, newer noninvasive techniques are being developed that allow indirect screening of pathogens through feces, urine, hair, and saliva (Real and

Biek 2007). Carcass collection and testing is another way to estimate disease prevalence for diseases such as rabies that have a nearly 100% mortality rate in most carnivores.

However, one must correct for bias when collection of carcasses is non-random (Nusser et al. 2008).

Household surveys are another method that can be used to reconstruct animal interactions. In a study by Craft et al. 2016, 512 households from 40 villages around the greater Serengeti ecosystem were questions about the presence of wild carnivores near their village, the number of domestic animals in their household and interactions between domestic and wild carnivores. The researchers mention that there are limitations to this method such as interactions being skewed by what villagers are willing to report or able to actually observe (e.g. difficult to make nighttime observations) which may lead to underestimation of interaction events. However, they concluded that the household

85 surveys were a useful tool to quickly assess interactions among a wide variety of species at the domestic animal-wildlife interface and that the method could be conducted over a relatively large area with reasonable accuracy (Craft et al. 2016). They found that the abundance rankings derived from their questionnaires correlated with previously collected night transect abundance rankings thereby suggesting that respondents did not substantially over- or underreport carnivore species (Craft et al. 2016). This study was able to produce valuable information about interspecific carnivore behavior and how human culture and local and regional geography play a complex role in domestic and wild carnivore interaction risk. Unfortunately, this method is still unable to provide the high resolution data required for disease modeling.

The development of new techniques in the field of molecular epidemiology have allowed the source of an infection to be identified which in turn allows actual transmission histories in wildlife populations to be established. Spatial spread among different host populations or geographic areas can frequently be determined from differences in genetic sequence data (Real and Biek 2007). For example, Real et al.

(2005) characterized the phylogeographic relationships among 83 isolates of fox rabies virus variants using nucleotide sequences from the glycoprotein-encoding glycoprotein gene (G-gene). From this sequencing data they were able to determine that the original gene sequences (often nested within adapted sequences) identified in the most recent common ancestor proceeded to diverge into two subgroups (Real et al. 2005). Each subgroup represented one of two arms of an advancing wave of rabies virus variants from northern Ontario into southern and eastern Ontario (Real et al. 2005). Several smaller waves of divergence were able to be identified as well. These molecular methods that

86 allow identification of transmission histories are valuable tools when it comes to disease modeling, especially spatial disease models.

Multi-Species Infectious Disease Models for Wildlife Populations:

Key Definitions

Prevalence: proportion of positive cases in a population at a particular point in time

(Viana et al. 2014).

Reservoir- one or more epidemiologically connected populations or environments in which the pathogen can be permanently maintained and from which infection is transmitted to the defined target population (Haydon et al. 2002).

Target Population- is the population of concern or interest (Haydon et al. 2002).

Non-Target Populations- all other potentially susceptible host populations than the target population that are epidemiologically connected directly or indirectly to the target population and could potentially constitute all or part of the reservoir (Haydon et al.

2002).

Critical community size- the minimum size of a closed population within which a pathogen can persist indefinitely (Haydon et al. 2002).

Maintenance Population- a single host population equal to or larger than the critical community size in which a pathogen can persist over the long term (Haydon et al. 2002,

Viana et al. 2014).

Non-Maintenance Population- populations smaller than the critical community size in which a pathogen is unable to persist (Haydon et al. 2002).

Metapopulation: a set of populations that are connected by transmission and can be comprised of structured populations of the same species, populations of different species or a combination of the above (Viana et al. 2014).

Reservoir capacity: a measure of the potential of a host metapopulation to support long- term pathogen persistence in the absence of external imports (Viana et al. 2014).

Patch value: a measure of the contribution of individual populations to the reservoir capacity of a metapopulation (Viana et al. 2014).

Source Population: any population that transmits infection directly to the target population. These populations may themselves be maintenance populations or may constitute all or part of a transmission link from a maintenance population to the target population (Haydon et al. 2002).

Maintenance Community: any set of connected host (sub) populations that combined can maintain a pathogen over the long term (Viana et al. 2014).

Minimal Maintenance Community: a maintenance community in which all subsets are non-maintenance. A maintenance population is also a minimal maintenance community

(Viana et al. 2014).

Stuttering chain: a pattern of cases in the form of short chains where transmission among hosts occurs but is too weak to support endemic or epidemic transmission (Viana et al.

2014).

Asymmetric Host Community: A pathogen-host system in which there is no back- transmission from the target host to the reservoir (Fenton and Pedersen 2005).

The Reservoir

According to the definitions listed above, the populations that make up a reservoir may be the same or different species as the target population. Some reservoirs can be simple and consist of only a single non-target maintenance host population while other can be made up of a more structured set of connected host subpopulations making up a maintenance community. If the target population is a maintenance population and able to transmit to and become infected by an outside source population, then it can be part of a maintenance community (Haydon et al. 2002). To include a non-target population in a reservoir, evidence of transmission to the target population, direct or indirect, must exist

(Haydon et al. 2002). If the reservoir is a maintenance community and all populations within the maintenance community are directly or indirectly connected to each other, then the size of the reservoir has no upper limit (Haydon et al. 2002) (Figure 6).

Figure 6. Examples of Target-Reservoir System Structure

(taken from Viana et al. 2014).

Though non-maintenance host populations connected to the target population have often been overlooked in the past, their role as part of the reservoir is essential. These populations often play a crucial role in transmitting the pathogen to the target population.

For example, foot and mouth disease can persist indefinitely in African buffalo herds yet recent epizootics in impala, a non-maintenance population, suggest that they may constitute an important source of infection for cattle, the target population (Bastos et al.

200). Similarly, brucellosis was nearly eliminated in cattle in the United States however spillover of the disease from cattle to elk and bison (both non-maintenance populations in most areas) led to epizootics in those species that now result in regular spill back from elk to cattle thus complicating control efforts (Rhyan et al. 2013). Foot and mouth disease can be maintained within African buffalo independent of impala just as brucellosis can be maintained in cattle independent of elk and bison, however excluding these populations from the reservoir would be excluding a major component of transmission.

Management Methods and when to Target the Reservoir

Policies to manage infection in a target-reservoir system generally contain elements of three different tactics including target control, blocking or barriers, and reservoir control (Haydon et al. 2002). Target control involves directing efforts solely within the target population with no reference to the reservoir. Blocking or barrier tactics work to stop transmission between source and target populations. Reservoir control involves focusing efforts on controlling infection solely within the reservoir. Each tactic requires a progressively increasing level of understanding of reservoir structure (Haydon et al. 2002). Though one needs to be able to identify the source populations within the reservoir in order to carry out blocking or barrier tactics, detailed knowledge of complex

91 reservoir dynamics is most important for reservoir control. Control measures will be ineffective if they are directed at elements of the reservoir that are neither maintenance hosts nor transmitters of the pathogen to the target population (Haydon et al. 2002).

A good example comes from jackal rabies virus transmission in Zimbabwe where jackals may be important components of the reservoir as a maintenance or non- maintenance population (Bingham et al. 1999). Rhodes et al. 1998 used disease models to show that jackals could not maintain rabies independent of domestic dog populations however a study by Bingham et al. 1999 found that it is most likely that jackals are able to maintain rabies independent of domestic dogs in some parts of the country but not others due to varying ecological conditions. This was found to be what was occurring in

South Africa with jackal rabies (Zulu et al. 2009). It is known that domestic dogs can maintain the rabies cycle independent of jackals. The question of whether or not jackals are able to maintain the rabies virus independent of domestic dogs alone or as part of a maintenance community is important because if dogs are the only maintenance population in the reservoir, effective vaccination campaigns targeted at dogs alone should be able to successfully eliminate human rabies from Zimbabwe (Haydon et al. 2002).

However, if jackals make up all or part of a maintenance community independent of dogs, eliminating rabies will only be successful if jackal rabies is also controlled

(Bingham et al. 1999, Haydon et al. 2002).

Framework of Basic Theoretical Transmission Pathway Models

Fenton and Pedersen (2005) created a framework for classifying pathogen transmission in multi-host pathogens based on between-and-within species transmission rates. Haydon et al. (2002) had previously tried to create a similar framework that

92 assumed the outcome of infection between a target host species and a second host species, termed species complex, was largely dependent on the size of the two populations. Fenton and Pedersen argue that focusing solely on host density ignores many key features of emerging diseases such as between and within species transmission rates as well as ecological conditions acting on both host and pathogen to influence contact between susceptible and infected individuals (e.g. weather patterns, activity cycles).

The Fenton and Pedersen model is a deterministic model that relies on a simple 2- host 1-pathogen structure establishing 3 thresholds for pathogen and host persistence separating 4 classes of disease outcomes (Fenton and Pedersen 2005). These thresholds are based on between-and-within species net transmission rates. They then examine ecological factors that determine the location of various host-pathogen systems within the framework on a continuum. Ultimately, they compare this model to a stochastic model in order to consider what characteristics of the hosts and pathogen define the dynamics and likelihood of an emerging infectious disease. Their model assumes that the pathogen of interest is endemic within host population 1 (H1) resulting in all individuals being either susceptible (S1) or infected (I1) (Fenton and Pedersen 2005). This assumption does not account for individuals that may develop immunity, recover or die from the pathogen as a result of infection but rather assumes that once an individual is infected, they will remain infected until death. This is true of some pathogen infections (eg. herpes virus, HIV) but not all. However, the SI model will work for simplification. The second target host population, host population 2 (H2), then enters the community and can become infected by the pathogen. This is the target host population. Their model assumes that since the

93 pathogen is already well established in H1, the number of susceptible and infected individuals in H1 remain unchanged by the arrival of H2 (Fenton and Pedersen 2005).

Again this assumption is an oversimplification however it will work for the purpose of the model. Host population 1 is considered to be a maintenance host population with the potential to be a reservoir for host population 2. Host population 2 may or may not be a maintenance host. Assuming density dependence, the following equations make up the model: dS2/dt= rH2(1-H2/K) – (f22 + f12) dI2/dt= f22 + f12 –dI2

H2 = number of individuals in population 2 I2 = number of infected individuals in population 2 S2 = number of susceptible individuals in population 2 r= reproductive rate K= carrying capacity d= death rate of the infected hosts f22= function of net within-species transmission rate for the second population f12 = function of net between-species transmission rate between first and second population fi,j = βi,jIiS2 where βi,j is the per capita rate of transmission from species i to species j β= transmission coefficient

The number of susceptible individuals in H2 at any given point in time is equal to the product of the birth rate of H2 and the population size of H2 multiplied by the number of individuals in H2 that the environment cannot support subtracted from the sum of the within H2 and between H1 and H2 species transmission rates. The number of infected individuals in H2 at any given point in time is equal to the death rate in H2 multiplied by the number of infected individuals in H2 subtracted from the sum of the within-H2 and between-H1 and H2 species transmission rates. Therefore, the net rate of transmission from H1 to H2 (f12) is dependent on the size of the susceptible target population (S2), the

94 size of the reservoir (I1), and the level of exposure and susceptibility of H2 (β12) (Fenton and Pedersen 2005). The SI model can be seen in Figure7.

Within the Fenton and Pedersen framework there are four possible outcomes for the target host population (H2): 1) no infection; 2) infected but unable to sustain the pathogen; 3) infected and able to sustain the pathogen; 4) infected and driven to extinction by the pathogen (Fenton and Pedersen 2005). Each of these outcomes is separated by one of three thresholds: 1) invasion; 2) persistence; 3) host extinction

(Fenton and Pedersen 2005) (Figure 7). In the first outcome, the pathogen is unable to invade the population. In the second outcome, the invasion threshold is reached and the pathogen invades H2 but cannot be sustained because it does not reach the persistence threshold. The pathogen reaches the persistence threshold in the third outcome and is able to be sustained within H2. In the fourth outcome, the population reaches the extinction threshold in which the pathogen invades H2 and drives it to extinction. In the model, density effects are combined with per capita rates of infection to express each threshold in terms of the magnitude of the net transmission rates between-and-within species

(Fenton and Pedersen 2005).

Figure 7. SI Models and the Four Outcomes for the Target Host Population

(taken from Fenton and Pedersen 2005)

Fenton and Pedersen (2005) lay all possible scenarios out on a 2-dimensional continuum with between-species transmission rates on the x-asis (infection of H2 by H1) and within-species transmission on the y-axis (H2 ability to sustain pathogen alone).

Within the continuum, they describe four possible scenarios. The first scenario is what they call “spillover” and relates to disease outcome 1 listed above. In this case, both the between-and-within species transmission rates are too low to sustain the pathogen. Here,

96 although infections can occasionally occur through transmission from H1 to H2, they are short-lived and cannot be sustained within the target population.

The second scenario is termed “apparent multi-host pathogen” and relates to disease outcome 2. In this case, the within-species transmission rate remains low but the between-species transmission rate increases and allows the pathogen to exceed the invasion threshold. This results in persistent infection in H2 as a result of frequent between-species transmission from H1. However, once introduced into H2 the pathogen cycle is not long-lasting. Here, there is the potential for high prevalence in the target host population H2 that may give the appearance of a true multi-host pathogen, however lack of within-species transmission means that the disease cannot be maintained in the absence of H1 (Fenton and Pedersen 2005). In both the first and second scenarios, H1 is considered to be a maintenance population while H2 is a nonessential target population.

An example of an apparent multi-host pathogen would be rabies in wild carnivore communities in the Serengeti. Here, prevalence of the rabies virus in wild carnivores has been found to be relatively high suggesting the potential for maintenance within these communities. However, it was found that the observation was due to spill-over of rabies from domestic dog populations that sometimes initiated short-lived chains of transmission in other carnivores with none of these carnivore communities being able to maintain the pathogen independently (Lembo et al. 2008).

The third scenario is the” true multi-host pathogen” which relates to disease outcome 3. Here, both within-and-between species transmission rates are high and therefore the pathogen is able to be maintained independently within both H1 and H2.

Each population can be considered a maintenance population. The fourth and final

97 scenario is what they call a “potential emerging infectious disease” and relates to disease outcome 4. Here, the within- H2 transmission rate is high but the between-H1 and H2 transmission rate is low. The result is that the target host population (H2) is able to maintain pathogen transmission when transmission from H1 does occur, however between-H1 and H2 transmission is so low that H2 is rarely exposed to the disease. This event can only occur if there is a between-species transmission event. Once this between- species transmission event occurs however, the high within-species transmission rate has the ability to drive the host to extinction (Fenton and Pedersen 2005). Fenton and

Pederson note that this might occur when a barrier to infection, such as geographic isolation, is overcome by an anthropogenic change such as deforestation, resulting in the creation of new opportunities for exposure. They also state that this scenario may be the region of greatest concern for the future considering a single transmission event can have devastating consequences due to high rates of within-species transmission in the target host (e.g. SARS, Nipah virus) (Fenton and Pedersen 2005). The continuum can be seen in

Figure 8.

Figure 8. The Fenton and Pedersen Continuum

(taken from Fenton and Pedersen 2005)

Sources of Variation within the Continuum/ Stochasticity

The location of a host-pathogen system within the continuum will be strongly influenced by both host and pathogen factors. For example, mode of pathogen transmission can play a significant role in the likelihood that a pathogen will encounter a new host. In wild primates and humans, pathogens that rely on direct contact for transmission tend to be associated with higher host specificity (Fenton and Pedersen

2005). In this case, the host- pathogen system would rely more on within-species transmission than between-species transmission and would fall close to zero on the between-species transmission axis and higher on the within-species transmission axis.

Similarly, host-parasite systems may segregate along the axes due to host taxonomy.

Viruses are more likely to jump from one host to another if the phylogenetic distance between them is small (Fenton and Pedersen 2005). Thus, host-pathogen systems with hosts that are more closely related will fall higher on the between-species transmission axis and closer to zero on the within-species transmission axis.

It is important to remember that host-pathogen systems are not static and may move across the continuum either because of ecologic or evolutionary shifts of the host or pathogen (Fenton and Pederson 2005). For example, the evolution of a pathogen can have a significant impact on the likelihood of disease emergence by increasing the pathogen’s basic reproductive number (R0). Fenton and Pedersen (2005) provide the example of avian influenza. Avian influenza had emerged several times in the human population previously but had only very limited ability to transmit from human-to-human. Outbreaks were rare and isolated as in the spillover scenario in the continuum. Once recombination occurred between strains resulting in acquisition of human-specific respiratory epithelium receptors, human-to-human transmission became frequent. As a result of the increased within-species transmission rates, the virus was able to be sustained within the human population moving avian influenza dynamics from the spillover scenario to the true multi-host pathogen scenario (Fenton and Pedersen 2005).

Opportunities for introduction of stochasticity in the real world can appear to be endless. Other factors to consider include behavioral patterns of the host, geography, niche partitioning and land-use change. Additionally, emerging infectious diseases tend to occur at discrete intervals. Such variability in time is best represented by a stochastic model instead of a deterministic model where events occur on a continuous time scale

100

(Fenton and Pedersen 2005). In order to account for some of this stochasticity, Fenton and Pedersen (2005) developed a stochastic analog from the previously described deterministic model. Results from the stochastic model were then compared to the deterministic model in order to explore where differences occurred.

The Stochastic Model and Comparison

Fenton and Pedersen (2005) created their stochastic model for a 2-host-1- pathogen system using a discrete-time (jumping from one event to the next with no change in-between events) (Matloff 2008) Monte Carlo simulation model (series of algorithms that rely on repeated random sampling). In this model, each event (births, deaths, between-species transmission, within-species transmission) occurred probabilistically and the next event was then chosen at random according to those probabilities. The model was run 100 times using different combinations of within-and between-species transmission rates. Infection status of the target host (H2) was then measured in three ways: 1) the mean prevalence over time; 2) the proportion of time the pathogen was absent from H2 because the pathogen faded out; 3) the proportion of runs in which the pathogen drove the host to extinction.

Overall the results from the Fenton and Pedersen (2005) stochastic model were in agreement with the community epidemiology continuum based off of their initial deterministic model. The stochastic model showed that low between-and-within-species transmission prevented the pathogen from persisting in the target host as in scenario

1“spillover.” Increasing the between-species transmission rate while the within-species transmission rate remained low, or even at zero, lead to a gradual increase in both prevalence of infection and the proportion of time the pathogen was present in H2. The

101 regular, high exposure to the pathogen from the reservoir was able to give the appearance of endemic infection even though the pathogen could not be sustained within H2, thus matching up with scenario 2 “apparent multi-host dynamics.”

Increasing the within-species transmission rate appeared to have little effect on H2 until a threshold was reached at which point the pathogen could persist (persistence threshold). The pathogen was able to become endemic regardless of input from between- species transmission. This is different than the deterministic model which relies on some contact with H1, even if that amount of contact is small. Though spatially, increasing the within-species transmission rate without increasing the between-species transmission rate falls into the scenario 4 “potential emerging infectious disease” area of the continuum, the result of establishing an endemic state aligns more closely with scenario 3 the “true multi-host pathogen” state. The major difference between the stochastic model results and the deterministic model was that in the stochastic model, increasing either between- or within-species transmission rates could lead to a point where the host was driven to extinction. The deterministic model showed that H2 extinction could only occur if the between-species transmission rate was high enough (host extinction = β12 > dr/(d – r)).

However, this was poorly represented in the continuum where it appeared that extinction

(scenario 4) could only occur if within-species transmission was high. The stochastic model showed that if H2 is a poor transmitter of the disease, frequent contact between H1 and H2 can drive H2 to extinction and if H2 is a good transmitter of the disease but between-species transmission is low, then H2 can still be driven to extinction. Spatially these results fall in the areas that align with scenario 2 “apparent multihost pathogen”

(between-species transmission is high and within-species transmission is low) and

102 scenario 4 “potential emerging infectious disease” (within-species transmission is high and between-species transmission is low) in the continuum however the result of driving the host to extinction matches best with the scenario 4 “potential emerging infectious disease” outcome. In more simplified terms, the final result showed that not only can increasing within-species transmission rates without high between-species transmission rates lead to extinction, but increasing between-species transmission rates without high within-species transmission rates can also lead to extinction. Overall this gets quite complicated. Viana et al. 2014 revised the Fenton and Pedersen continuum to simplify the potential host-pathogen outcomes.

Continuum Updated and Re-Explained

Viana et al. 2014 redesigned and built on to the Fenton and Pedersen (2005) continuum by creating six zones (A-F) defined in relation to the relative magnitudes of the force of infection from one or more sources, which they place on the x-axis, and the basic reproduction number of the pathogen within the target population (R0), which they placed on the y-axis. Zones “A” and “B” are what they label as “dead end cases.” In zone

“A,” the R0 in the target population is 0 and the force of infection from outside populations is minimal. Here, the interval between cases in the target host population is longer than the infectious period for any single case and cases are not directly linked

(Viana et al. 2014). The invasion threshold has not been reached and each case is the result of a single introduction from one species to another. Viana et al. 2014 use Lyme disease in humans as an example to describe zone “A.” As the force of infection from outside populations increases while R0 remains 0 (zone “B”), cases in the target population become observed more frequently. At higher values, cases can co-occur in

103 time and space but still remain epidemiologically unlinked and genetically distinct.

Therefore, multiple introduction events could occur during the same time period but this does not mean that transmission is occurring within the target population. West Nile

Virus in humans is used as an example for a pathogen that would fit in this zone (Viana et al. 2014).

Zones “C” and “D” are labeled as “stuttering chains.” Zone “C” is characterized by target populations in which limited within-species transmission can occur with an R0 that is 0

Viana et al. 2014 use the example of cattle brucellosis in Yellowstone National Park to describe this zone. A combination of zones “A” and “C” make up scenario 1 in the

Fenton and Pedersen paper. In zone “D,” the force of infection from outside sources increases while R0 remains 0

(2005) paper. Because between-species transmission is high and there is still some within-species transmission occurring, multiple “outbreaks” can occur at the same time creating the illusion of endemnicity as the pathogen appears to persist (Viana et al. 2014).

Overall, zones “B” and “D” combined make up scenario 2 in the Fenton and Pedersen

104 paper. Viana et al. 2014 note that systems in which R0 approaches 1 are particularly vulnerable because even small changes, through pathogen evolution or changes in the target population structure, can cause R0 to exceed 1 and results in either endemic or epidemic situations.

Zones “E” and “F” are labeled as “large outbreaks.” Zone “E” covers a variety of outcomes based on how far above 1 the R0 reaches. The persistence threshold has been reached in all cases. If R0>1, then any spillover event is able to result in a substantial epidemic. If the R0 is only slightly larger than 1, then stochastic extinction of the pathogen or fadeout will likely occur (Viana et al. 2014). However, if the R0 is significantly greater than 1, then the outbreak takes off and there are three possible outcomes: 1) the target population sustains a major epidemic followed by pathogen extinction; 2) the target population sustains a major epidemic after which the pathogen is maintained in an endemic state; 3) control measures within the target population are able to effectively reduce the R0 to below 1 thus averting a major epidemic and resulting in pathogen extinction(Viana et al. 2014). This description is consistent with the “potential emerging infectious disease” scenario from the Fenton and Pedersen (2005) paper.

Influenza pandemics in humans are used as an example to represent this zone. This explanation accounts for the lack of agreement between the stochastic model and the deterministic model in the Fenton and Pedersen (2005) paper. In the Fenton and Pedersen paper, the deterministic model showed that scenario 4 “potential emerging infectious disease,” in which within-species transmission is high and between-species transmission is low, resulted in extinction of the host population. However, their stochastic model showed that an endemic state was able to be achieved here. Viana et al. 2014 show that

105 either of these outcomes could result when within-species transmission is high (or R0 is high despite low force of infection from other species) and between- species transmission is low. They then describe zone “F” as a state where the R0 >1 and the force of infection from the reservoir is large. This agrees with scenario 3 “true multi-host pathogen” in the

Fenton and Pedersen paper. Viana et al. 2014 state that in this situation, fadeout is unlikely. The result is an endemic state with occasional epidemics. The example that they provide to explain zone “F” is cattle foot-and-mouth disease in sub-Saharan Africa

(Viana et al. 2014). This updated continuum does not explain a scenario where an outbreak can take off in the complete absence of within-species transmission as was found in the Fenton and Pedersen stochastic model. This is an area that requires further investigation and evidence from the field.

Viana et al. 2014 note that these different situations are likely to be hard to distinguish using only patterns of incidence and prevalence. They suggest using spatiotemporal data and pathogen genetic sequence data along with sophisticated analytical techniques such as state-space modelling in order to properly examine these patterns. The Viana et al. (2014) continuum can be seen in Figure 9.

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Figure 9. Continuum updated by Viana et al. 2014

(taken from Viana et al. 2014)

Methods Used to Identify Reservoirs and Establish Transmission Dynamics

As previously mentioned, there are a variety of methods that can be used to identify reservoirs. In very general terms, the existence of a reservoir can be confirmed when all transmission between target and non-target populations is eliminated thus preventing the target population from being able to sustain pathogen transmission

(Haydon et al. 2002). However, in order to properly allocate resources to prevent transmission between the target population and the reservoir, one needs to know what the reservoir is in advance. The best first step in identifying a reservoir is to accumulate epidemiologic evidence. Quantitative data on risk factors for infections can be obtained

107 through case-control and cohort studies. For example, a cross-sectional study conducted in Uganda sampled animal rearing households in 10 endemic villages for Tunga penetrans ( a parasite) to identify the major animal reservoir. They found that pigs had the highest prevalence and widest distribution of tungiasis and concluded that pigs were the major animal reservoir in the area (Mutebi et al. 2015). Similarly, a questionnaire- based, retrospective case-control study of 174 Swedish cats showed that hunting mice was a risk factor for Borna disease and that rodents might be potential reservoirs of the virus (Berg 1998). Although these associations may suggest a link between reservoir and target populations, further evidence is required to establish the identity of a reservoir

(Haydon et al. 2002).

Unfortunately, because it is difficult to isolate virus from wildlife populations, even patterns of incidence and prevalence can be difficult to obtain. Typically, such data must be obtained from indirect measures such as longitudinal seroprevalence surveys

(Viana et al. 2014). Interpretation from seroprevalence surveys is often unreliable due to cross-reactivity (non-specific antibody reaction induced by a pathogen that is not the pathogen of interest), declining antibody titers, cut-off thresholds used to distinguish positive and negative reactions, and difficulty with detectability of antibodies (Viana et al. 2014). There are, however, advanced modelling techniques (such as Bayesian process models) that can detect cross-species transmission to identify which host species is the most likely source of infection (Viana et al. 2014).

Identifying hosts that are naturally infected with a pathogen can also help to identify potential reservoir populations (Haydon et al. 2002). Natural infections can be determined by either identifying a current infection through isolating the infectious agent

108 or its genes from the host, or by identifying previous infection through antibody detection. Detecting a pathogen in secretions or tissues can provide supportive evidence that transmission to the target population can occur however this evidence is not definite.

Even if experiments can demonstrate that transmission is possible, it may not occur in nature for a variety of reasons such as distribution, behavioral or social reasons (Haydon et al. 2002).

Genetic characterization of pathogens from different populations provides stronger evidence for identifying key components of a reservoir. Patterns and associations can be identified through applying phylogenetic methods to sequences, using random amplified polymorphic DNA, and restriction fragment length polymporhism data

(Haydon et al. 2002). Because accumulation of mutations takes place on approximately the same timescale as transmission, if there is sufficient genetic variability of the pathogen within the reservoir, such genetic data should be able to distinguish events of rare spillover and subsequent transmission in the target from repeated introductions due to a high force of infection from outside the target population (Viana et al. 2014).

Spillover and subsequent within-species transmission from a source population to the target population would show slightly different genetic sequences in the target population due to accumulation of mutations as the pathogen adapts to the target.

Repeated between-species introductions into the target population from the same source population would show less sequence variation from the source population in the target population due to fewer mutations. Between-species introductions from a different outside source population may then produce sequences in the target population that are distinct from both the first source population as well as what would be produced by any

109 within-target transmission that may occur. This would allow one to be able to distinguish pathogen dynamics between zones “B” and “C” and zones “D” and “E” in the Viana et al. 2014 continuum.

Critical community size (CCS) is a metric that can be used to help identify a reservoir population by identifying whether or not a population is able to maintain a pathogen. As previously mentioned, CCS is defined as the minimum size of a closed population within which a pathogen can persist indefinitely (Haydon et al. 2002).

Therefore, in order to be a maintenance population, the size of the host population must be greater than the CCS. Once a population in which persistence is to be measured has been defined, there are many challenges to using CCS to determine maintenance.

Persistence and maintenance are measures that are sensitive to epidemiological and demographic dynamics. In order to simplify this obstacle, CCS is most commonly discussed in the context of single well-mixed populations, even though such populations are rare in natural systems (Viana et al. 2014). Another challenge is defining persistence because any estimate of CCS will be affected by the choice of the persistence metric. One way to define CCS is to relate it to the probability of extinction within a given time frame or the time until a give proportion of introductions (or simulations) have gone extinct

(Viana et al. 2014). Once a definition has been determined, the next step is to estimate the

CCS. Viana et al. 2014 state that there are three main approaches used to estimate CCS:

1) empirical observation in which incidence data is plotted against population size; 2) analytic expressions; 3) stochastic computer simulations in which compartmental models are assigned parameter values that are used to generate distributions of persistence times for populations of different sizes from which CCS can be estimated. Unfortunately,

110 analytic expressions exclude many processes relevant to CCS such as latency, spatial heterogeneity, seasonality, and age structure, and non-exponential infectious periods

(Viana et al. 2014). Stochastic simulation studies assume a linear relationship between population size and recruitment which is unrealistic in natural systems (Viana et al.

2014). Though CCS can be a very useful metric, better methods for estimation must be developed.

Calculation of reservoir capacity is yet another method that can provide insight into pathogen persistence when dealing with a reservoir that is made up of multiple connected populations (metapopulation). As previously defined, reservoir capacity is a measure of the potential of a metapopulation to support pathogen persistence in the absence of contact from outside host species. This method is based on ecological theory and applied to mathematical models (Viana et al. 2014). One of the benefits of using this method is that it uses patch values which represent the relative contribution of each population to pathogen persistence. These patch values can be used to prioritize populations when designing interventions (VIana et al. 2014). The modelling framework includes three processes including within-population processes, transmission between populations, and community-level persistence. The model is used to investigate one of these processes when it is possible to parameterize the other two (Viana et al. 2014). This measure can be regarded as a measure of effective host abundance weighted to take into account factors such as population size, connectivity, and other factors that may influence fadeout within a populations and transmission between them. The general equations used to calculate reservoir capacity rely on invasion and fadeout rates being treated as functions of the infection status of other populations as well as population size and

111 transmission rates (Viana et al. 2014). The probability that infection is present in a given population at a point in time is dependent the ratio of population invasion events to disease fadeout events (Viana et al. 2014). These events are balanced at equilibrium.

Reservoir capacity also suggests a persistence threshold in which the minimum expected ratio of invasions to fadeouts across populations is greater than 1 (Viana et al. 2014).

Once a potential reservoir has been identified, using interventions as quasi- experiments can help confirm or deny whether the population in question is truly a reservoir host that is essential to transmission and maintenance of infection in the target- reservoir system (Haydon et al. 2002, Viana et al. 2014). In many cases, disease-control programs can effectively act as intervention studies. For example, if dog rabies is effectively eliminated due to vaccination and jackal rabies still persists, an effective program for rabies elimination will likely need to include vaccination of jackals.

However, because dogs are a maintenance population, control within that population is not wasteful (Haydon et al. 2002). Viana et al. 2014 warn that though using intervention as quasi-experiments can provide valuable insight into the target-reservoir system, it is also important to take into account the fact that these interventions can alter the target- reservoir transmission dynamics which may lead to difficulty in distinguishing causes and effects of the intervention. Independently, none of these methods can provide strong evidence for reservoir identification. However, using a combination of these methods together can provide valuable insight into the reservoir-target system.

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Existing Scenario in the Serengeti Ecosystem:

Reservoir Identification and Multi-Species Pathogen Study Designs

An early study conducted in 1995 sought to identify the role of wild and domestic animals in maintaining rabies in the Serengeti region of Tanzania (Cleaveland and Dye

1995). Samples were collected from records of reported rabies cases in the Serengeti

District (SD) from 1977-1994 and from the Loliondo and Ngorongoro Districts from

1985-1994. Additionally, passive surveillance was carried out beginning in 1991 for the

Loliondo Game Control Area (LGCA), 1992 for the Ngongoro Conservation Area

(NCA), and 1993 for the Serengeti District. This surveillance consisted of veterinary officers, park rangers and research scientists reporting suspect rabies cases in domestic and wild animals in addition to the collection of brain samples when possible. Large numbers of serum samples from domestic dogs in the area were collected as well. Lastly, dog population sizes were estimated from ratios of people: dogs and from ratios of dogs: household in pastoralist areas of NCA. These ratios were estimated from questionnaire

surveys of a sample of 546 households.

Cleaveland and Dye (1995) conclude that domestic dogs are likely to be the sole reservoir of rabies in the Serengeti ecosystem. They found that confirmed and reported cases were dominated by domestic dogs. They also found that 3 rabies virus isolates taken from domestic dog, African wild dog and cow were found to be antigenically and genetically indistinguishable (Africa 1b) canid-associated viruses (Cleaveland and Dye

1995). Historical evidence supported the role of dogs as the sole maintenance reservoirs of rabies virus in the Serengeti ecosystem through observation of the success of dog rabies control programs in the 1950s and 1960s that resulted in rabies elimination in parts

113 of the country from 1958-1977. During this time, no cases were reported in either dogs or wildlife. Lastly, out of 46 wildlife samples tested for rabies, only two tested positive.

Both of these samples were from bat-eared foxes which historically showed epidemiological patterns of explosive outbreaks lasting only 5 to 7 weeks followed by periods of no cases (Cleaveland and Dye 1995). This is consistent with short-lived chains of transmission rather than maintenance. Cleaveland and Dye suggest that wild carnivores in the Serengeti may not be able to maintain rabies transmission independently because it is a species rich conservation area where no single carnivore species can reach a sufficient density for disease maintenance to occur. This theory was later supported by a similar study in the Serengeti ecosystem discussed below (Lembo et al. 2007).They mention that throughout the world, most of the major wildlife reservoir hosts of rabies are opportunistic species (foxes, jackals, mongooses, raccoons) that live at relatively high densities in agricultural areas or close to human settlements.

One major finding from this study was that the threshold density for persistence of rabies virus in domestic dogs of the Serengeti is 5 dogs/km2 (Cleaveland and Dye

1995). They found that the SD had a dog density exceeding 5 dogs/km2 but that the

LGCA and the NCA have densities below 1 dog/km2. Therefore, dogs can only technically be considered maintenance reservoirs in the SD. This threshold was modified by Lembo et al. 2008 (discussed below) to 11 dogs/km2 based off of results from the SD.

They add that a domestic dog density of <5 dogs/km2 is less likely to maintain rabies cycles based off of their results from the LGCA and NCA areas (Lembo et al. 2008). Yet another finding was the existence of rabies antibody in healthy unvaccinated dogs.

Similar findings have been found in unvaccinated free-ranging African wild

114 dogs,blackbacked jackals, and Ethiopian wolves (Prager et al. 2012). From their mathematical models, Cleaveland and Dye (1995) concluded that though they cannot be sure what the seropositivity means, persistence in low-density dog populations is more likely if seropositives are infectious carriers rather than slow-incubators or immunes. It appears that this is still unknown (Prager et al. 2012).

Though they conclude that dogs are the sole reservoir of rabies virus transmission in the Serengeti District, Cleaveland and Dye note that the fact that bat-eared foxes in

South Africa are capable of maintaining rabies independently of dogs (Thomson and

Meredith 1993) in addition to findings of recurrent cases in the Serengeti highlight the need for further investigation of their role in the Serengeti. They also state that the predominance of domestic dogs among confirmed and reported cases in the Serengeti as well as other African countries may be an artefact of less intense surveillance and under- reporting of disease in wildlife and that measures need to be enhanced before definite conclusions can be made regarding wildlife reservoirs in the Serengeti.

A more recent study, Lembo et al. 2008, that attempted to identify reservoirs of the rabies virus in the Serengeti ecosystem is also one of the best examples of how to identify a reservoir in a multi-host system. This study investigated rabies reservoirs in the complex carnivore communities of the Serengeti National Park where the virus has been confirmed in 12 carnivore species. The researchers proposed 4 potential scenarios existing in the Serengeti ecosystem shown in Figure 10.

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Figure 10. Potential Existing Scenarios for Existing Target-Reservoir Systems in

the Serengeti Ecosystem

In all cases, dogs and other carnivore populations together make up the reservoir. Lembo et al. (2008) note that livestock and endangered wildlife populations can also be considered as target populations in addition to humans. A.) Humans serve as target population, dogs are a maintenance population capable of transmitting to humans and other carnivore populations, other carnivore populations act as a non-maintenance population capable of transmitting to both dogs and humans. B.) Humans serve as target population, dogs are a non-maintenance population capable of transmitting to humans and other carnivore populations, other carnivore populations act as a maintenance population capable of transmitting to both dogs and humans C.) Humans serve as target population, both dogs and other carnivore populations serve as non-maintenance

116 populations each capable of transmission to and receiving transmission from one another as well as transmission to humans D.) Humans serve as target population, both dogs and other carnivore populations serve as maintenance populations each capable of transmission to and receiving transmission from one another as well as transmission to humans (Lembo et al. 2008).

Wild carnivore communities in the Serengeti comprise 26 species including

Canidae and Herpestidae species implicated as independent maintenance hosts of rabies in parts of southern Africa (Lembo et al. 2008). To discover the situation in Tanzania,

Lembo et al. (2008) synthesized data from long-term epidemiological records, phylogenetic analyses of virus isolates from a both domestic and wild species and statistical analyses of spatiotemporal patterns of rabies incidence. The study area consisted of 3 zones with different community structures. The first zone was in the

Serengeti National Park (SNP) where diverse wildlife communities existed however domestic dogs are extremely rare. The second zone was in the Serengeti District (SD), west of Serengeti National Park, where a combination of multi-ethnic agro-pastoralist communities and high-density dog populations co-existed. The third zone was the

Ngorongoro District (ND) to the east of SNP which is a multiple-use controlled wildlife area made up of low-density pastoralist communities and very low-density dog populations. A greater abundance of wild carnivores was observed in the ND than the SD

(Lembo et al. 2008). The researchers note that there is no physical barrier separating any of the wildlife-protected areas and human settlements.

The data for this study come from long-term passive surveillance beginning in the year 1987 in the SD. Early passive surveillance efforts relied on data collected from

117 sightings of sick and dead carnivores reported by a network of veterinarians, rangers, scientists, and tour-operators. Cases included animals that were snared, injured from another or unknown source, observed with signs of disease, or found dead with unknown cause. Due to this generalized case definition, it would seem that significant reporting error could have occurred. However, Lembo et al. (2008) did calculate reports of rabies diagnosis against the gold standard fluorescent antibody test for samples collected during the years 2002-2005 and found that greater than 74% of animals reported as suspect rabies cases were confirmed to be positive. This could imply that earlier reporting efforts showed a similar trend. Additionally, samples were collected from some of the suspect animals and tested for rabies starting in 1992. Passive surveillance data were also obtained through veterinary records, district hospitals, and medical dispensaries. They looked at evaluations by clinicians for bite injuries to humans from suspected rabid dogs as well as how many vaccinations (PEP) had been administered. Bite injury incidence was calculated against human population sizes using national census data. Active surveillance was also carried out in SD on and off starting around 1993 by using livestock field officers stationed in randomly selected villages to collect information on rabies cases from village leaders, teachers, dispensary staff and local healers.

The researchers were able to obtain high resolution data on spatial and temporal patterns of disease by carrying out contact tracing starting in 2002. Contact tracing was carried out by making visits to incidents involving biting animals (both bite victims and owners of biting and bitten animals) (Lembo et al. 2008). These incidents were identified by using hospital and medical dispensary records as well as case reports from livestock offices and community-based surveillance. Incidents were mapped and witnesses

118 interviewed to make a diagnosis and obtain case-history information. When multiple incidents involving suspected rabid wildlife were reported on the same or consecutive days within neighbouring areas, the researchers assumed a single animal was involved. In addition to all of this data, the researchers collected brain stem samples from suspect rabies cases and carnivore carcasses, whatever the apparent cause of death.

Results from tracing 1,255 suspected rabid animals in SD and ND showed that cases in wildlife were sporadic and coincided with outbreaks in domestic dogs (Lembo et al. 2008). Results from phylogenetic analysis of brain samples after the nucleoprotein genes were sequenced (published in Lembo et al. 2007) showed that all 57 rabies virus specimens collected from a range of species in the study area fell within the Africa 1b group of canid-associated viruses with no clusters being observed (Lembo et al. 2008).

This lack of species-specific virus–host associations in the Africa 1b group that was detected shows a high degree of genetic relatedness between virus samples from different species which indicates frequent cross-species transmission. Their analysis using statistical parsimony techniques were also consistent with both within- and between- species transmission events. However, they found that within-species transmission was more frequent than between-species transmission. Incidence patterns indicated that spillover of rabies from domestic dog populations sometimes initiates short-lived chains of transmission in other carnivores. This is consistent with the “stuttering chain” scenario described in the continuum created be Viana et al. 2014).

The researchers state that clusters of wildlife cases that coincided with outbreaks in domestic dog populations are consistent with chains of transmission that are not sustained and contrast with incidence patterns in wild carnivores that are believed to

119 maintain rabies through independent cycles elsewhere in Africa (Lembo et al. 2008).

Independent cycles tend to be characterized by cases that can be sustained over long periods of time. Lembo et al. 2008 conclude that the evidence shows that domestic dogs are the only maintenance population of rabies in the Serengeti while other carnivores are nonessential to maintenance. However, because other carnivores transmit disease to the target populations and frequent transmission between-and-within such species may occur, they do make up part of the reservoir. This indicates that scenario “A” in the diagram of potential scenarios existing in the Serengeti system is what is truly occurring (Lembo et al. 2008). These results show that vaccination and control of rabies in domestic dogs of the Serengeti will be able to eliminate rabies in all other species.

A study published in 2009 in the same area of Tanzania including many of the same authors as the Lembo et al. 2008 study produced results that are highly valuable to disease models. This study produced one of the most extensive datasets on individual transmission events assembled in an animal population. In particular, they were able to calculate a very reliable measurement for the basic reproductive number for rabies in dogs. Dogs were the focus of this study due to results from the Lembo et al. 2008 study that identified dogs as the sole maintenance reservoirs of rabies virus in this system. They state that rates of transmission are usually inferred from population patterns of disease incidence however population level analyses do not capture between-individual variation in transmission (behavior, genetics, immune status, environmental and stochastic factors)

(Hamspon et al. 2009). The researchers overcame this limitation by using contact tracing to track case-to-case transmission directly during a rabies outbreak in northern Tanzania

(Serengeti and Ngorongoro). They were able to record more than 3,000 potential

120 transmission events between 2002 and 2006 and were able to reconstruct case histories of over 1,000 suspect rabid animals. This allowed for heterogeneity in latency, movement patterns, and biting tendency of infected individuals to be quantified.

The basic reproductive number was estimated through three methods consisting of evaluation of infection histories, reconstruction of epidemic trees based on the spatiotemporal proximity of cases, and from the exponential rate of increase in cases starting at the beginning of the epidemic (Hampson et al. 2009). They were also able to identify several other important parameters that they applied to their calculations of R0.

The incubation period was found to be 22.3 days with an infectious period of 3.1 days on average(Hampson et al. 2009). The biting behavior of rabid dogs during the course of infection was found to be highly variable with a mean of 2.15 bites per rapid dog

(Hampson et al. 2009). The probability that an unvaccinated dog would develop rabies after being bitten by an infectious animal was found to be 0.49 (49%) (Hampson et al.

2009). This number was multiplied by the average number of dogs bitten per rabid dog

(2.14 ≈2 ) in order to calculate the R0 based on case history which was found to be

R0=1.05 (Hampson et al. 2009). Because the researchers were able to collect detailed data on the location and timing of transmission events they were able to calculate the spatial infection kernel (0.88km) and the generation interval (24.9 days) which are the distances and times between source cases and their resulting infections respectively (Hampson et al. 2009). Using this information they were able to reconstruct transmission networks and calculate the R0 by from those networks. The average number of secondary cases per rabid dog during the period of exponential grows (before vaccination) from these reconstructions produced an R0 of 1.1 in the Serengeti District and 1.3 in Ngorongoro

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(Hampson et al. 2009). Lastly, the researchers fitted a curve to incidence data over the same interval of exponential epidemic growth to produce an R0 of 1.2 in the Serengeti and 1.1 in Ngorongoro (Hampson et al. 2009). In order to compare their results and provide an even more robust analysis, an R0 value was estimated from the intrinsic growth rate of outbreaks of domestic dog rabies around the world from at least 13 different countries for comparison. They obtained values between 1.05 and 1.85 which are consistent with their estimates from northwest Tanzania (Hampson et al. 2009).

This study also provided detailed information on dog demographics in the area including density estimates of 9.38 dog/km2 in the Serengeti District and 1.36 dogs/km2 in Ngorongoro (Hampson et al. 2009). Interestingly, results showed that R0 remained the same between the two areas regardless of dog density suggesting that canine rabies transmission in Tanzania is not density dependent (Hampson et al. 2009). Additionally, they did not find any differences in R0 estimates from other outbreaks around the world which represent a wide range of population densities. They do state that density effects may be difficult to detect with such a low R0 value. In order to investigate whether or not they would be able to detect density effects with their data, they simulated outbreaks using their epidemiological parameter estimates but varied R0 from 1 to 2 while maintaining individual variance in biting behavior. The results suggest that if only a small number of epidemics were sampled, any underlying relationship might not be apparent

(Hampson et al. 2009). The final major conclusion from this paper was that calculations of the critical vaccination threshold (Pcrit) in Tanzania showed that only 20% of the population must be vaccinated in order to eliminate rabies in the population and even in areas where R0 was higher, the critical threshold only rose to 40% (Hampson et al. 2009).

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The commonly accepted value for the amount of vaccination coverage in order to achieve long-term rabies elimination is 70% (World Health Organization 2004). The researchers state that their simulations did show small outbreaks occurring by chance even when coverage exceeded their critical threshold values. They attribute this finding to the rapid rate of turnover in the domestic dog population making it more difficult to maintain vaccination immunity in-between vaccination periods (Hampson et al. 2009). In this case, the researchers suggest that herd immunity requires a larger proportion of the dog population to be vaccinated (Hampson et al. 2009). Taking into account demographic parameters of the dog population, they found that annual vaccination campaigns should aim to vaccinate 60% of the dog population in order to avoid coverage following below the critical threshold (Hampson et al. 2009). They suggest annual pulse vaccination in domestic dogs in Tanzania in order to maintain 60% coverage (Hampson et al. 2009).

Throughout their studies they found that villagers reduced the overall average infectious period by around 16% by killing rabid dogs however there were no consistent declines through time in the number of bites by rabid dogs (Hampson et al. 2009). Therefore, vaccination is the best method to eliminate canine rabies in Tanzania and they believe that, according to their results, elimination is very possible (Hampson et al. 2009).

Spatial Models for Multi-Species Pathogen

Craft et al. 2008 were able to create a stochastic and spatial susceptible-infected- recovered (SIR) model of disease transmission involving two-and three host-species communities with widely ranging social structures. Their goal was to examine whether differences in territorial social structure affect the spatial and temporal pattern of disease outbreaks and if the time course of any resulting epidemics is sensitive to different rates

123 of within- vs. between-species interaction. In previous studies, pathogen transmission in multiple hosts has often been considered an additional form of heterogeneity (Craft et al.

2008). As a result, these models divided the total host population into subpopulations where between-subgroup transmission occurs at a different rate than within-subgroup transmission (as described by Fenton and Pedersen 2005 and Viana et al. 2014) (Craft et al. 2008). However, Craft et al. (2008) state that such approaches do not account for differences in how host species might vary in their response to infection, have varying contact patterns based on social behavior and have different spatial distributions across the landscape. In most cases, it is assumed that each host population is well-mixed and heterogeneities due to social organization tend to be ignored. In this study, the researchers attempted to mimic a 1994 Serengeti Canine Distemper Virus (CDV) outbreak which infected a variety of carnivores with widely ranging social structures

(lion, spotted hyena, black-backed jackal). Lions have high within-species transmission and low between-species transmission rates, spotted hyena have both high within- and between- species transmission rates and black-backed jackal have lower within-species transmission and higher between-species transmission rates (Craft et al. 2008).

A set of 150 simulations for each combination of species were performed to examine the epidemic dynamics of this multiple-host system with contrasting social organizations (e.g. isolated vs. well connected territorial structures) characterized by different within- and between-group transmission rates (Craft et al. 2008). The habitat was divided into a two-dimensional grid of 625 patches with each patch containing a local population of each species. Group size was held constant across species and across social groups (10 individuals of each species on each patch) in order to isolate the effect

124 of social organization. Infections were introduced in a single individual at the edge of the grid to mimic a pathogen being introduced from domestic dogs at the edge of the park

(Craft et al. 2008).

Within the model, infection is spread within local populations, between different species occupying the same patch, and between any populations or species occupying neighboring patches. The model was designed so that the probability that a susceptible individual will be infected depends on the number of infections in its own social group, interspecific transmission within the same patch, and intra- and interspecific transmission from neighboring patches (Craft et al. 2008). For each species, the within- and between- group transmission rates were varied to confirm that the results were representative of the overall range of possible outcomes. Two 'who acquires infection from whom' matrices

(WAIFW) were created to characterize the force of infection between individuals of each group (Craft et al. 2008). Because previous studies in other species have identified an R0 of 2.8 for CDV (Swinton et al. 1998), Craft et al. (2008) adjusted the R0 value slightly to match the species being examined. They defined R0 as 2.2 and had species within- and between - patch transmission rates selected so that the R0 value in a single species habitat was equal to that number (Craft et al. 2008). Two values were used, one high and one low (0.2, 0.01 respectively), to represent coupling (Craft et al. 2008). Coupling was the measure the researchers used to vary interspecific interaction.

The model was able to successfully mimic the discontinuous spatial pattern of lion deaths observed in the Serengeti lions under a reasonable range of parameter values, but only when one to two other species repeatedly transmitted the virus to the lion population (Craft et al. 2008). Results showed that when interspecific

125 transmission/coupling rates were higher, the combined population of species acts as a single super species incorporating the strongest parameters of each species (Craft et al.

2008). This resulted in larger susceptible host populations, more hosts being infected and the pathogen having a significantly higher impact in species that could not sustain an outbreak in isolation (Craft et al. 2008). They also found that adding a second species that is more effective at transmission produces an amplification effect while adding a less- effective second species can cause a dilution effect (Craft et al. 2008), similar to what was described by Ostfeld, Keesing and Pongsiri in chapter 3 section 1. The researchers state that in the 1994 CDV outbreak, hyenas and/or jackals could have acted as amplifying species by spreading CDV through the more isolated lion prides causing long distance jumps in infection among prides (Craft et al. 2008). However, because the outbreak in lions was discontinuous and erratic, Craft et al. 2008 state that low interspecific contact rates could have accounted for the extensive coverage of CDV infection and erratic spatial spread seen in the Serengeti lions (Craft et al. 2008)

Three generalized conclusions for directly transmitted multi-host pathogens were made from their results: 1) differences in social structure can significantly influence the size, velocity and probability of a multi-host epidemic; 2) social structures that permit higher intraspecific neighbor-to-neighbor transmission are the most likely to transmit disease to other species; 3) species with low neighbor-to-neighbor intraspecific transmission are most vulnerable to interspecific transmission (Craft et al. 2008).

Network Models to Examine Within-and-Between Species Transmission Rates

A study by Craft et al. (2009) used data from the same CDV outbreak in lions of the Serengeti ecosystem described above to create a network model. This study provides

126 a good example of how network models can help discern cases driven by within-species transmission vs. between-species transmission. The study sought to answer whether the outbreak was driven by lion-to-lion transmission alone or involved multiple introductions from other sympatric carnivore species. Though their previous study, Craft et al. 2008, was able to identify the effects of varying within-and-between species transmission rates, the focus was on the effects of spatial heterogeneity. From the 2008 study, they could not determine whether an outbreak was restricted to hyenas, jackals and lions, or a larger combination of susceptible species but rather that low interspecific contact rates could have accounted for the extensive coverage and erratic spatial spread of canine distemper virus seen in the Serengeti lions (Craft et al. 2008). The study was not able to identify within-or-between species transmission as the primary driver of the outbreak. This issue is important to identify for control measures.

Serengeti lions are generally considered to be a non-maintenance population for

CDV due to low population numbers (Craft et al. 2009). Craft et al. 2009 state that for direct intervention in non-maintenance populations, it is critical to determine whether or not the population is “percolating” or “non-percolating.” Craft et al. (2009) state that

“percolating” means to reach epidemic proportions. If a non-percolating population experiences repeated introductions of diseases from sympatric populations, control measures should focus on preventing new introductions of disease into that population.

However, in the case of an epidemic as a result of a percolating population, or a population where within-species transmission is high enough to reach epidemic proportions, strategies should also target transmission within the host population (Craft et al. 2009). Incorrect interventions can waste resources and cause harm to wildlife.

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Craft et al. (2009) again question traditional disease models stating that they are misleading because they assume that populations are fully mixed and that all contacts are spatially proximate however, endangered species often live in groups and defend territories against conspecifics thus exhibiting population structure that is neither fully mixed nor geographically localized. Their populations show structure in which groups are highly intra-connected and more loosely interconnected based on complex movement and behavioral patterns (Craft et al. 2009). The researchers relied on stochastic susceptible- exposed-infectious- recovered contact network modeling to answer their primary research question. They note that few studies of wildlife provide adequate empirical information to parametrize a network however, in this case, the Serengeti Lion Project provided a long-term dataset including decades of daily observations of behavior and movement

(Craft et al. 2009). Using this data, the researchers created one of the most detailed and biologically realistic epidemiological network models of a wildlife population to date that includes pride composition, movement of nomads (long-distance roaming lions) and contact rates between prides and nomads (Craft et al. 2009).

The model created was a territory network at its core in which prides were grouped into single units or nodes (Craft et al. 2009). The edges (connections to the nodes) were created between prides with adjacent territories. The territory distance between any two prides was defined as the shortest path connecting their nodes and prides were designed to contact one another as a function of territory distance. Nomadic lions (long-distance dispersers as opposed to shared males who increase disease transmission between neighbouring prides) then moved as a type of variance gamma process contacting prides in their vicinity according to empirical estimates (Craft et al.

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2009). Through the stochastic SEIR simulations of transmission through the lion network created, they were able to monitor disease spread for the entire population and within a geographically restricted subset (Craft et al. 2009). Any given pride or group of nomads moved through the four disease classes as a unit. The analysis was based on 200 simulated epidemics at 60 transmissibility values (T) between 0.0 and 0.3 (Craft et al.

2009). For each run, a new lion population network was generated randomly. A sensitivity analysis was then carried out by running 200 replicate simulations at each of

50 transmissibility values (Craft et al. 2009).

The results showed that the model failed to identify a range of transmission values that could have produced an epidemic that was both as large and as slow as the 1994 outbreak (Craft et al. 2009). Their analysis strongly suggested that, although lions are sufficiently well-connected to sustain epidemics of CDV-like diseases (they are a percolating population), the 1994 epidemic was driven by cases of multiple spillover from sympatric carnivore species including jackals and hyenas (Craft et al. 2009). Their analysis is supported by the finding that cases of CDV during this outbreak in lions, hyenas, bat-eared foxes, and domestic dogs were all infected by the same strain (Haas et al. 1996, Roelke-Parker et al. 1996). The researchers state that their results demonstrates that wildlife populations do not always fulfill assumptions of classical epidemiological models and they emphasize an understanding of both network structure and sampling caveats when constructing disease models for wildlife populations.

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Spotted Hyenas of the Serengeti: Asymptomatic Rabies Carrier Theory and Transmission

Parameters

A very interesting case was presented by East et al. (2001). This study reported the existence of an asymptomatic carrier state in spotted hyena populations of the

Serengeti in which the animals were able to eliminate the virus from their body after exposure. They found a frequency of rabies exposure in the spotted hyenas of 37.0% in addition to RT- PCR identifying rabies RNA in 13.0% of hyenas (East et al. 2001).

However, exposure neither caused symptomatic rabies nor decreased survival among the hyena groups observed (East et al. 2001). The researchers state that sequence divergence between isolates from canids and viverrids was substantial with sequences from the spotted hyena samples more closely related to Europe/Middle Eastern isolates than to canids and viverrids occupying the same area (Africa 1b) (East et al. 2001). Their finding of repeated and intermittent presence of virus in saliva of 45.5% of the seropositive hyenas indicated the existence of this carrier state (East et al. 2001). However, this study has not been able to be replicated and no virus was actually isolated from the samples which is stronger evidence than RNA detection. It has also been suggested that results from the phylogenetic analysis may have been the result of laboratory contamination.

Lembo et al. 2007 state that this finding is difficult to explain. They explain that out of the diagnostic samples from 41 hyenas that they collected in the same ecosystem, four tested positive for rabies and all were identified as Africa 1b strains (Lembo et al. 2007).

They note that clinical signs of hyenas infected with this variant are typical and that rabies morbidity and mortality has been reported in hyenas in other parts of Africa.

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According to them, there is no doubt that Serengeti hyenas can die when infected with dog rabies and that rabid hyenas pose a severe risk to humans and other mammals

(Lembo et al. 2007). Lembo et al. 2007 do agree that with their intra- and interspecific behavior, wide-ranging commutes outside of protected areas, scavenging in agricultural areas, and predation on domestic dogs, hyenas do most likely act as a critical link in rabies transmission between domestic and wild carnivore populations in the Serengeti and elsewhere.

The East et al. (2001) study did produce valuable results related to hyena demography and behaviors that are associated with rabies transmission. The data used for this study came from a 13-year study from May 1987-June 2000 (published in Hofer and

East 1995) of spotted hyenas of the Serengeti combine with disease surveillance data from the carnivore community within the Serengeti National Park. Several hundred individually recognized Serengeti hyenas in three social groups were regularly monitored in terms of behavior over this time resulting in over 15,000 hours of observation. The

Hofer and East (1995) researchers observed that Serengeti hyenas live in large social groups, or clans, with a mean number of 45 adults and subadults at a density of 0.8 adults and subadults per km2 (Hofer and East 1995). These groups consist of linear female and male dominance hierarchies in defended territories with female philopatry (tendency to stay in one place) and male dispersal (Hofer and East 1995). They live in matriarchal communities with adult females possessing the highest social status. Cubs are reared in a communal den inside the clan. During 46-62% of the year, all clan members other than den-bound cubs regularly travel (commute) on average about 40 km from their territory to forage in areas containing large migratory herds (Hofer and East 1995). Territory size

131 is roughly 5km2 on average (Hofer and East 1995). All of this information significantly contributes to identifying routes of rabies transmission from movement patterns related to social structure and age, density and aggressive interactions.

East et al. (2001) were able to apply surveillance data to this observational data and provide very important insight into rabies transmission in the spotted hyena population of the Serengeti. They were able to calculate an oral contact rate, which they defined as the rate at which individuals from various age and social classes had their open mouths licked by other clan members. The rate was calculated from focal observation of

32 cubs, 9 subadults, 12 immigrant males, and 26 adult females that had been observed for 100 hours (East et al. 2001). They report that the mean contact rate was 73 ± 5 min for more than 100 hours observed at the communal den (East et al. 2001). This is slightly confusing considering the fact that this is not an actual rate. There is a figure that shows oral contact rate for the different group (cubs around 0.28, subadults around 0.49, immigrant males around 0.20, and adult females around 0.67) (East et al. 2001) however units are not clearly defined in the figure or anywhere throughout the paper. It does show, and is supported by statistical testing, that cubs and immigrant males had significantly lower oral contact rates than adult females (East et al. 2001). This result indicates that transmission rates among adult females may be the highest making them higher risk individuals. In order to support oral contact rate as a good measure for rabies exposure, they were able to show that seroprevalence patterns matched with the patterns observed from oral contact (East et al. 2001). They found that rabies virus titers increased with oral contact rates in 18 individuals for which both titers and contact rates were recorded.

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Females showed the highest exposure patterns along with the highest rabies virus titers

(East et al. 2001).

In addition to contact rates, the study identified several other measures including bite rates and a basic reproductive number. Though the bite rates themselves were not actually reported, the study did statistically show that rates at which adult males and females received bite wounds from conspecifics increased with social status and were similar for both sexes (East et al. 2001). The R0 value that they calculated was 1.9 (East et al. 2001). They performed a sensitivity analysis to examine how changes in annual adult mortality rates compared with changes in the resulting R0 value and results showed that R0 was insensitive to the precise value of annual adult mortality rate because for each

1% change in this value, the value of R0 changed only 0.40% to 0.58% (East et al. 2001).

East et al. 2001 state that the simple model used to estimate this R0 value assumes a weakly homogenous host population structure. Because exposure appeared to be linked to age and social status, the Serengeti hyenas are unlikely to meet this assumption.

Therefore, they caution that their R0 is simply a rough estimate rather than a precise one.

Lastly, they state that there is currently no evidence that the hyena isolate circulates within canid or viverrid species. Though this study has many caveats and limitations to interpretation, it does seem to provide information on spotted hyena population structure and behavior that can be applied to the study of rabies transmission in this population as well as to other spotted hyena populations elsewhere.

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Rabies Transmission in Southern Africa and Kenya:

Background

A study by Swanepoel et al. 1993 provides a very detailed description of how rabies cases moved throughout southern Africa along with reports of rabid animals in different

African countries throughout the 20’th and late 19’th century. They start by describing the first confirmed outbreak of rabies in southern Africa which was believed to have followed the importation of an infected dog from England in 1892 in the eastern Cape

Province of South Africa and was brought under control in 1894. This was followed by a series of repeated outbreaks travelling upward throughout South Africa, Zimbabwe,

Zambia, Namibia, Angola, Botswana, and all throughout Africa over the course of the century. This review provides early evidence of independent rabies strains in wildlife species.

The researchers discuss the existence of endemic rabies in viverrids in South

Africa which was confirmed in 1928 by laboratory testing after a child became infected after receiving a bite from a yellow mongoose (Swanepoel et al. 1993). Since that time, they state that rabies cases in yellow mongoose occurred widely on the interior plateau of the country with spillover of infection to cattle and a variety of other animals. Clusters of cases identified in south-western Zimbabwe in the slender mongoose are said to be suggestive of the existence of an independent cycle of transmission in the species (Foggin

1988). Veterinary investigators were well aware that viverrid rabies in South Africa differed fundamentally from what they termed “classical European type dog rabies” in that there were sporadic cases in dogs but no real tendency for the infection to spread among them (Swanepoel et al. 1993). In northern South Africa, the yellow mongoose

134 accounted for 60% of all animals in which the disease had been recorded from 1932-1992 while in the Cape Provinces, yellow mongoose only accounted for less than 30% of rabid animals recorded during the same period (Swanepoel et al. 1993). Monoclonal antibody studies finally confirmed that virus strains associated with endemic viverrid rabies can be distinguished from the canid strain (King et al. 1991).

The paper goes on to discuss the spread of rabies in dogs in central Namibia, northern Botswana, Zimbabwe and northern Transvaal (northern South Africa) which was followed by the emergence of rabies of jackals and cattle in. In Zimbabwean jackal populations (which consist of the side-striped jackal and the black-backed jackal), rabies occurred in large, dense, moving epidemics in commercial farming areas. Bingham

(2005) adds that the jackal index cases of the epidemics were usually associated with cases in dogs thus indicating that these epidemics were initiated by dog rabies cycles however, once initiated, the epidemics were maintained independent of dogs (Swanepoel et al. 1993). The reviewers note that several of the jackal outbreaks that occurred in

Zimbabwe since 1965 were independent of outbreaks in dogs and took place well away from known centers of infection from other species (Swanepoel et al. 1993).

An outbreak in kudu antelope occurred in central Namibia from 1977-1985 apparently involving oral spread of infection between individuals (Swanepoel et al.

1993). Cases of black-backed jackal rabies were noted prior to the epidemic and it is thought that the outbreak in kudu was initiated by interactions with black-backed jackal

(Swanepoel et al. 1993). Several cases of rabies in bat-eared fox were recognized in in

Namibia from 1967 onwards. Cases in bat-eared foxes emerged in South Africa in the

1970’s and since then have been a distinct problem in the northern Cape Province and

135 throughout the west coast (Swanepoel et al. 1993). The authors state that there is strong evidence to suggest that there is independent spread of rabies in the bat eared fox, however they do not provide details on what that evidence is. The first case of rabies in spotted hyena populations of South Africa was reported in 1986 (Swanepoel et al. 1993).

Currently in Zimbabwe, Swanepoel et al. (1993) report that the side-striped jackal occurs over most of the country however reports of rabies in this species have only come from the northern and eastern parts of the country. The black-backed jackal occurs in the southern, western and central regions (where there is lower rainfall) and supports rabies cycles in all of these areas (Swanepoel et al. 1993). In South Africa, rabies is circulates through black-backed jackals in the northern border areas and through bat-eared fox populations in the western regions and the southwest Cape (Swanepoel et al. 1993,

Sabeta et al. 2003).

Other Lyssaviruses including Lagos bat virus, Mokola and Duvenhage, generally associated with shrews, bats, and rodents in Africa, are known to have caused isolated cases of disease in South Africa. In the 1980’s, Lagos bat virus was able to be isolated from 13 epaulleted fruit bats in South Africa (Meredith and Standing 1981, Swanepoel et al. 1993). Duvenhage was then isolated from a bat in 1981 in South Africa and later in

Zimbabwe (Van Der Merwe 1982, Foggin 1988). Mokolo virus was also identified in

Zimbabwe and South African rodents through detection with monoclonal antibodies

(Foggin 1988, King 1991). The researchers conclude that infrequent reporting of sylvatic rabies in developing nations is largely due to deficient monitoring of the disease in wild vertebrates rather than a lack of transmission (Swanepoel et al. 1993).

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Mongoose Rabies

The case of mongoose rabies (previously termed viverrid) in South Africa has been an area of study for a long time. A study by King et al. 1993 used anti-nucleoprotein monoclonal antibodies to examine reaction patterns in 83 virus isolates taken from various canid and mongoose species throughout South Africa. Results showed two major reaction patterns, one was confined to viruses from canids and the other to viruses from mongoose (King et al. 1993). The canid rabies reaction was observed in the majority of canid isolates with the exception of five that were consistent with the mongoose rabies reaction (King et al. 1993). The researchers state that these isolates are suggestive of spillover of mongoose rabies into canids (King et al. 1993). Additionally, five of eight bovine isolates were consistent with the canid rabies reaction and three were consistent with the mongoose rabies reaction showing that both variants are able to infect bovine species (King et al. 1993). No variation in the canid reaction pattern was observed however, there was considerable variation in the mongoose reaction patterns (King et al.

1993). This result is consistent with long-established infection in which mutation and evolution of host and parasite (mongoose species and rabies virus) may have allowed the emergence of viral variants. The researchers conclude that these results support previous findings that isolates of mongoose origin differ from other genotype 1 rabies viruses as well as suspicion that indigenous rabies has been present in mongoose species of South

Africa for a long time (King et al. 1993).

Several years later, a study carried out by von Teichman et al. (1995) tested 24 samples from yellow mongoose, domestic dog, black-backed jackal and bat-eared fox from South Africa. More advanced molecular techniques than antigenic typing were

137 carried out by sequencing the cytoplasmic domain of the glycoprotein and the G-L intergenic region of the viral genomes (non-protein encoding region which is highly susceptible to random mutation). The G-glycoprotein is the major antigen involved in eliciting protective immunity and the hydrophilic region of the glycoprotein trimer, the cytoplasmic domain, is not only structurally limited by attachment to the cytoplasmic bilayer but is also involved in the initiation of virus assembly and budding (von

Teichman et al. 2005). As previously mentioned, the G-L intergenic region is the most variable region of the rabies genome and most likely to be sensitive enough to demonstrate recent evolutionary events (Nel et al. 2005).

Results of the von Teichman et al. (1995) study showed that rabies virus clustered into two groups. The canid group included isolates from dogs, jackals and bat-eared foxes that shared a high degree of nucleotide sequence conservation amongst these isolates

(von Teichman et al. 1995). There were three atypical canid isolates that were found to cluster with the mongoose isolates rather than the other canid isolates which the researchers believe is suggestive of incidents of spillover from mongoose to canids (von

Teichman et al. 1995). The mongoose group included isolates from mostly mongoose as well as the three atypical canids consisting of one jackal, one domestic dog, and one bat- eared fox. Consistent with results presented by King et al. 1993, a high degree of nucleotide divergence was found in the mongoose isolates with an average difference of

14% from one another (von Teichman et al. 1995).

In a more recent study, Nel et al. (2005) examined the genetic relationships of 77 rabies virus isolates of the mongoose biotype collected from more than 19 different species isolated in South Africa and Zimbabwe. The results were consistent with what

138 was found in von Teichman et al. 1995 however more detailed. The researchers sequenced and comparing 592 nucleotides encompassing the cytoplasmic domain of the glycoprotein and the G-L intergenic region of the viral genomes.

Nel et al. 2005 provide some historical and ecological information about rabies in mongoose species in southern Africa to provide some perspective. They mention that different habitats and distribution patterns among the mongoose species result in the principal reservoir for the mongoose viruses in South Africa being the yellow mongoose while the slender mongoose is the principal reservoir in Zimbabwe (Nel et al. 2005). The researchers note that mongoose rabies is also enzootic in parts of the Caribbean where it appears to be the main manifestation of rabies virus. South African historical records show that mongoose rabies may have been described since the early 1800s which was long before canine rabies was introduced (Nel et al. 2005). Mongoose rabies has been reported to contribute roughly 44% of total confirmed rabies cases in South Africa, 57% in the Caribbean Islands of Grenada and 2% in Zimbabwe (Nel et al. 2005). The researchers emphasize that such figures are not a true reflection of the incidence of these viruses but more likely reflects the attention given to wildlife rabies in these countries.

The results of their pairwise alignment and comparison of cognate sequences showed that a high degree of variation with respect to the G–L intergenic sequence exists between the different mongoose isolates and that the majority of base differences were found to be transitions (point mutation in from either a purine to a purine [A,G] or a pyramidine to another pyramidine [C,T]) (Nel et al. 2005). The genetic heterogeneity among the mongoose virus isolates ranged between 1.9 and 27% (average = 18%)(Nel et al. 2005). Analysis of the cytoplasmic domain of the glycoprotein showed that all

139 mongoose virus isolates were found to lack the first of the two polyadenylation sites for the G-genes that are typically found in traditional fixed rabies strains that are used for a reference (ERA and Pasteur vaccine strains) (Nel et al. 2005). Phylogenetic analysis of sequence alignments that were carried out for all samples allowed for statistically supported distinction of five different clusters of virus isolates. These clusters were designated group numbers 1-5. Groups were made up of isolates originating from a diverse group of host species. All groups were found to be composed of isolates from at least three different host species and 8 on average (Nel et al. 2005). The existence of the genetically distinguishable groups could not be attributed to their different hosts of origin

(Nel et al. 2005). However, the groups did reflect the geographical origin of the virus isolates with each group corresponding to a separate geographical region (Nel et al.

2005). The researchers mention that despite the involvement of a large variety of wild carnivore species, it is evident that the yellow mongoose is the principal vector for the mongoose rabies virus biotype in South Africa (Nel et al. 2005). The two phylogenetic groups that did not contain isolates from yellow mongooses could be mapped to geographical regions where this species occurs at low density or not at all (Nel et al.

2005).

Nel et al. 2005 believe that some social aspects of the yellow mongoose may play a role in the variability observed among isolates. They mention that the majority of matings are conducted by a dominant male while young will remain as helpers with their parents. This behavior tends to promote genetic homogeneity in a restricted area, but genetic heterogeneity, or “islands,” between larger geographical regions (Nel et al. 2005).

Additionally, the researchers note that it the passage of viruses within different host

140 species may enhance the evolution of viruses as they continually adapt when being cycled among different hosts species (Nel et al. 2005). They conclude that their data indicate diverse origins and separate evolutionary paths for these viruses across this geographical domain in addition to lending strong support to the historical view that mongoose rabies, unlike the canid viruses, is indigenous to southern Africa and has been present in the region for at least 150 years (Nel et al. 2005).

Nel et al. (2005) stress the significance of improved surveillance and knowledge of the distribution of antigenic and genetic virus variants as important components of an efficient and economic rabies control program. Experiences in North America and

Western Europe are of much value by showing that, after succeeding with the control of rabies in dogs, a reciprocal marked increase of the disease in wildlife occurred with strains of the virus specifically adapting to various host species, such as raccoons, skunks, arctic foxes and a variety of others (Nel et al. 2005). As a result, they believe that specific and dedicated methods, biologicals and programs for the control of rabies in the wildlife of these African regions needs to be enhanced (Nel et al. 2005).

In 2007, a study by Davis et al. investigated the evolutionary dynamics of canine rabies variants in comparison to the mongoose variants. In order to carry out this investigation, a coalescent-based (tracing back to common ancestor) analyses of the G-L inter-genic region was performed allowing for rate variation among viral lineages through the use of a relaxed molecular clock (which relies on the viral nucleotide substitution rate) (Davis et al. 2007). Results showed that the mongoose rabies virus variants are evolving more slowly than canid rabies virus variants with mean evolutionary rates of 0.826 and 1.676 x 10-3 nucleotide substitutions per site, per year,

141 respectively (Davis et al. 2007). Additionally, as predicted in previous studies, mongoose rabies demonstrates older genetic diversity than canid rabies with common ancestors dating to 73 and 30 years, respectively (Davis et al. 2007). The researchers also found that while mongoose rabies virus has experienced exponential population growth over its evolutionary history in Africa, populations of canid rabies virus have maintained a constant size (Davis et al. 2007). Overall, the researchers conclude that despite circulating in the same geographic region, these two variants of rabies virus exhibit striking differences in evolutionary dynamics which are likely to reflect differences in their underlying ecology (Davis et al. 2007). They believe that the absence of a formal and effective control program for mongoose rabies in combination with the underground lifestyle of the yellow mongoose has enabled this biotype to sustain a slow, but long- term, population growth in the central plateau of South Africa (Davis et al. 2007).

These studies show that the mongoose rabies virus variant has existed historically for a longer period of time in southern Africa than canid rabies allowing for mutations resulting in sequence divergence among the lineages to occur. They also show that the mongoose rabies virus variants have a different origin than canid rabies virus variants.

Through the use of more recent molecular methods, Davis et al. (2007) were able to show that the rate of nucleotide substitution of the mongoose rabies virus has been slow and progressive over a long period of time while nucleotide substitution rate in canine rabies virus has been more rapid and recent.

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Rabies in Wild Canids of South Africa: Black-Backed Jackal (Canis mesomelas) and Bat-

Eared Fox (Otocyon megalotis)

The previous studies discussed do not distinguish canid species. However, studies that targeted canid species specifically in detail using methods with more discriminatory power, such as sequencing, have been able to identify variation within the canid group distinguishing various canid strains. A study by Nel et al. (1993a) compared three genome regions along with the phylogenies that were produced by each region. Eight canid rabies isolates comprising four dog, two jackal and two bat-eared fox viruses were analyzed in addition to four mongoose rabies isolates. They examined the cytoplasmic domain of the G-gene, the G- L intergenic region or pseudogene, and the antigenic domain II of the N-gene. The N- gene was selected to show long-term evolutionary trends while the other two regions show more recent evolutionary trends. Of particular importance, the researchers acknowledge that geographical factors were taken into account in the selection of isolates due to known associations between different host species and geographic location (Nel et al. 1993a).

Results showed that the South African canid isolates, with the exception of one atypical bat-eared fox isolate, were found to be closely related and could clearly be distinguished from all other rabies virus groups for which sequence data is available (Nel et al. 1993a). As expected, the four mongoose rabies isolates were shown to be genetically distant from the canid rabies virus group as well as from any other rabies viruses (or groups) for which sequence data was available (Nel et al. 1993a). Among the canid isolates, the antigenic domain II of the N-gene found a low overall mutation rate while the cytoplasmic domain of the glycoprotein showed that the first 30 nucleotides

143 displayed a great deal of variation (18.5%) with the remainder of the G-gene appeared to be very conserved (Nel et al. 1993a). The G-L intergenic region showed the most variation among the canid isolates with maximum and minimum variation of 5.7% and

1.2% respectively (Nel et al. 1993a). The most significant result was that phylogenetic analysis of the nucleoprotein gene in the canid isolates (with the exception of the atypical bat-eared fox isolate) showed that bat-eared fox, jackal and dog isolates cluster into three subgroups (controlling for geographical variables) (Nel et al. 1993a). However, overall the isolates were very closely related and could be distinguished from other rabies viruses of African origin. The atypical bat-eared fox isolate showed more than 13% sequence divergence in the cytoplasmic domain of the glycoprotein when compared with typical canid isolates (Nel et al. 1993a). The researchers conclude that this isolate either confirms a different and possibly archaic origin of South African mongoose rabies viruses or, more likely, suggests that spillover from other host species had occurred (Nel et al. 1993a).

A study published nearly ten years later by Sabeta et al. (2003) examined the genetic relationship of 89 rabies virus isolates from bat-eared foxes (n=9), jackal species

(n=41) and domestic dogs (n=39) collected from Zimbabwe and South Africa. The G-L intergenic region and the cytoplasmic domain of the glycoprotein were selected for analysis. Sabeta et al. (2003) provide background epidemiology before presenting their results. They note that domestic dogs were found to account for roughly 45% of all confirmed rabies cases between 1950 and 2000 in Zimbabwe (Foggin 1988) while jackals

(side-striped jackal and black-backed jackal) made up about 25% of all confirmed cases during the same period (Bingham et al. 1999). Dog rabies has generally been found to be maintained in rural areas of the country where the majority of the dog population is found

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(Butler 1998). In Zimbabwe, reports of rabies in the side-striped jackal have only come from the northern and eastern parts of the country while reports in the black-backed jackal have come from the southern, western and central regions (Sabeta et al. 2003). In

South Africa, rabies is found in domestic dogs in KwuZulu/Natal while it circulates through black-backed jackals in the northern border areas and through bat-eared fox populations in the western regions and the southwest Cape (Swanepoel et al. 1993).

Results showed that all canid viruses from both South Africa and Zimbabwe are closely related and are likely to have been derived from a single common progenitor

(Sabeta et al. 2003). Phylogenetic analysis showed that the percentage of sequence variation among these isolates was low on average with a mean of 3.4% (Sabeta et al.

2003). Overall bootstrap analysis of the phylogenetic tree demonstrated five different primary branches that were composed of four or more isolates and were supported by a bootstrap value of at least 66% (Sabeta et al. 2003). One of the groups was large consisting of 26 isolates. This group was made up of isolates from dogs and jackals from

Zimbabwe with the exception of one dog isolate from South Africa (Sabeta et al. 2003).

Within this group, two subgroups were loosely associated with geographical location in

Zimbabwe. One of these consisted of isolates from jackals and dogs from the southern parts of the country while the other was composed of isolates from central, eastern and northern parts (Sabeta et al. 2003). The remaining branches consisted of a group of isolates found in bat-eared fox populations of South Africa, a group of black-backed jackals from northern South Africa and southwestern Zimbabwe, a group of jackal species and dogs from central Zimbabwe, and a group of domestic dogs in north-eastern

Zimbabwe (Sabeta et al. 2003).

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Sabeta et al. (2003) mention that because the G-L intergenic region is often selected to examine the more recent evolutionary trends, phylogenetic groups may only appear significant after many years. They believe that the results of their study seem to show the beginnings of new evolutionary branches and that if these branches manage to persist, they may become more distinct over time (Sabeta et al. 2003). The authors conclude that their findings show that the canid rabies virus lineage is opportunistic within whatever canid host population is available and ecologically capable of sustaining prolonged cycles (Sabeta et al. 2003). They state that as individual canid host species tend to dominant different regions, phylogenetic branches that that appear to have species or geographical associations will continue to be observed (Sabeta et al. 2003). According to the researchers, the canid lineage has become well established throughout southern

Africa as a result of the highly efficient vector capabilities of the various canid hosts. It’s widespread nature and high prevalence makes it the most threatening variant for humans and domestic animals as well as the most likely to establish future cycles in new host species (Sabeta et al. 2003).

Nearly 4 years later another study, again including Sabeta as the first author, was able to identify independent rabies transmission cycles in the bat-eared fox throughout specific geographical zones (south-western region) of South Africa (Sabeta et al. 2007).

As previously mentioned, rabies was first diagnosed in the bat-eared fox population of

South Africa in the Limpopo province in the 1950’s (one introduction in 1955 and another in 1956) in areas bordering with Zimbabwe and Botswana (Swanepoel et al.

1993). Today the bat-eared fox is believed to be the principal maintenance reservoir in the drier western regions of this country (Swanepoel et al. 1993, Bingham 2005). In this

146 study a panel of 124 rabies isolates collected from bat-eared fox and other domestic species (suricate, water-mongoose, cape fox, black-backed jackal, unidentified mongoose) between 1980 and 2005 from the south-western region of South Africa were analyzed (Sabeta et al. 2007). Analysis consisted of examining the G-L intergenic region as well as the N-gene of each isolate.

Results showed that all of the viruses examined were closely related (G–L intergenic region = 97.5% and N-gene = 98.2%) however, they could be segregated into two major phylogenetic groups (Sabeta et al. 2007). The first group (lineage 1) was the smallest group and it was found to be mostly composed of viruses from the Southwestern

Cape (Sabeta et al. 2007). However, several (n=10) other viruses in this group were from the Northern Cape area (Sabeta et al. 2007). With the exception of four isolates (two suricate, one water mongoose, one other domestic dog), all of these viruses were bat- eared fox isolates suggesting that this lineage represents a distinct bat-eared fox rabies cycle (Sabeta et al. 2007). Within this lineage, all isolates were very closely related

(98%) (Sabeta et al. 2007). The remaining virus isolates grouping into lineage 2. These isolates were associated with areas to the north of those found in lineage 1 (Sabeta et al.

2007). This was a large group that could be sub-divided into four subclusters (i–iv).

Cluster iii composed isolates from a diverse array of species distributed widely throughout southern Africa (Sabeta et al. 2007). The authors suggest that this is evidence of a larger cycle of endemic southern African canid rabies virus (Sabeta et al. 2007).

They also report that subclusters i, ii, iv show signs of segregation into distinct bat-eared fox cycles of recent descent from the southern African dog and jackal rabies viruses found in cluster iii (Sabeta et al. 2007). This is in contrast to the isolates in the first

147 lineage which show a more fully evolved cycle of viruses circulating in the bat-eared fox with occasional spill-over into other small wildlife carnivores (Sabeta et al. 2007).

Sabeta et al. (2007) state that though the rabies virus isolates appear to have originated from the same domestic dog progenitor, the two separate introductions into the bat-eared fox population (the first occurring in 1955 in the Northern part of the country and the second taking place in 1956 in the north and Western Cape regions) indicate that these initial cases were not directly related (Sabeta et al. 2007). As a result, the two lineages identified in their study represent two separate introductions of canid rabies into the bat-eared fox populations of South Africa where only the second event was further disseminated within this species (Sabeta et al. 2007). Sabeta et al. (2007) also report that intra-species transmission appears to dominate rabies cycles in South African bat-eared foxes with only occasional inter-species events (Sabeta et al. 2007).

They believe that this is related to bat-eared fox behavior. Natural interspecific physical contact between bat-eared foxes and other canids is not common and is generally only reported in summer months when cubs are threatened by jackals approaching the dens too closely (Sabeta et al. 2007). Intraspecific transmission of the rabies virus is enhanced by certain bat-eared fox behaviors such as frequent oral–oral contact, living at high densities (1/km2 according to Nel et al. 1993b), frequent intermingling between several groups, and use of communal dens for several months (Nel et al. 1993b, Sabeta et al. 2007). The researchers did find that ten of the bat-eared fox isolates were found to be of the mongoose rabies biotype suggesting that though limited, cross-species transmission does occur (Sabeta et al. 2007). Sabeta et al. (2007) conclude that their results clearly show that rabies cycles in the bat-eared fox population can be

148 principally sylvatic in areas with low densities of human populations and domestic dog populations (and/or where dog vaccination coverage is very high) while there is frequent interaction of sylvatic and domestic dog cycles within areas more densely populated by humans and domestic dogs (Sabeta et al. 2007).

In a very similar study, it was found that black-backed jackal populations within certain regions of South Africa could maintain independent rabies cycles (Zulu et al.

2009). In this study, the researchers examined a panel of 123 rabies viruses obtained from two host species consisting 69 domestic dogs and 54 black-backed jackals from three provinces in South Africa (Limpopo, Mpumalanga and KwaZulu Natal) between 1980 and 2006. These samples were characterized by nucleotide sequencing of the cytoplasmic domain of the glycoprotein gene and the non-coding G-L intergenic region which was trimmed down to a 592 bp sequence.

Zulu et al. (2009) discuss that canid rabies is currently sustained in the Limpopo province by both domestic dogs and black-backed jackals and both domestic dogs and bat-eared foxes in the Cape region. It is thought that the black-backed jackal has co- existed with the domestic dog as a primary host of rabies in Limpopo since the 1950s

(Zulu et al. 2009). Dense black-backed jackal populations are common in bushveld ranches in western Limpopo and in in commercial farmland in the north and central province areas (Zulu et al. 2009). Reports from South Africa with regard to the issue of whether black-backed jackals are capable of sustaining rabies cycles independent of domestic dogs has been controversial. Some believe that jackal populations do not reach high enough densities to sustain the infection cycles in the absence of domestic dogs

149 while other believe that domestic dogs introduced rabies infection into jackals after which jackals were able to maintain their own cycles of infection (Zulu et al. 2009).

Pairwise alignment of the sequence data demonstrated that rabies viruses from northern South Africa were closely related with an intrinsic nucleotide sequence identity of 96.7% (Zulu et al. 2009). They also differed from the reference laboratory strain

(Pasteur virus) on average by 18.7% (Zulu et al. 2009). Phylogenetic analysis showed that the viruses from this region could be divided into six variants with the geographical distribution of these virus variants being determined by ecological conditions and land use (commercial farming) (Zulu et al. 2009). Black-backed jackal isolates made up more than 70% of isolates that grouped into the Limpopo/Northwest Province cluster demonstrating that this viral cluster has been well established in the black-backed jackal population and highlights the important role played by jackals in the transmission of the virus (Zulu et al. 2009). The most significant finding in this study was the identification of a cluster, cluster LP-I (western Limpopop), that was composed of virus isolates obtained exclusively from black-backed jackals and spanned over a 5-year period (2000-

2005) (Zulu et al. 2009). The data shows that this virus variant segregated into a distinct black-backed jackal rabies cluster with samples in this cluster originated from the bushveld ranches and commercial farmland (Zulu et al. 2009). The isolates in this cluster were very closely related with 99.8% average nucleotide sequence similarity (Zulu et al.

2009). The researchers state that the identification of this variant support arguments that black-backed jackal are capable of maintaining continuous rabies infection cycles independent of domestic dogs under specific ecological conditions (Zulu et al. 2009).

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Black-backed jackal aggressive interactions during territorial defense in addition to the wide ranging movement facilitate transmission of rabies. Sharing of resources such as water and large carcasses brings these animals into contact with other species introducing opportunities for transmission. Zulu et al. (2009) state that despite the immunization of domestic dogs, rabies epizootics could be sustained in jackal populations with the inevitable likelihood of re-infecting domestic dogs (Zulu et al.

2009). Therefore, wildlife rabies control strategies should complement existing domestic animal vaccination programs in areas where black-backed jackal rabies is enzootic such as Limpopo (Zulu et al. 2009).

Overall, these studies indicate that although all of the canid strains are closely related and have the same origin in the domestic dog, it appears that new lineages are beginning to diverge based on host species and geographical location. Species such as the bat-eared fox and jackal seem to be able to support rabies transmission independent of domestic dogs in certain geographical areas as long as certain ecological conditions exist

(e.g. low domestic dog population).

Jackal Rabies Transmission in Zimbabwe

Examining the canine rabies situation in Zimbabwe alone, Rhodes et al. (1998) sought to determine whether or not jackal populations in the country were capable of maintaining rabies independent of domestic dog populations using a basic mathematical model framework. They collected detailed field data through observations of side-striped jackals (Canis adustus) over a year-long study on 150 km2 of commercial farmland centered 40 km south-west of Harare, Zimbabwe. Side-striped jackals were the focus of this study because they made up the majority of rabies cases among jackal species in the

151 area (Rhodes et al. 1998). Trapping and radiotracking methods were used to examine the ecology of a total of ten males and nine females. Overall, a total of over 1700 hours of data was acquired (Rhodes et al. 1998).

The field data provides significant information that can be applied to rabies transmission dynamics. The study reported an estimated birth rate of 5.4 pups per pair born between August and January (seasonal forcing) (Rhodes et al. 1998). They also found that only two pups per litter survive past six months, territory-holding adults rarely live beyond six years and that the average lifespan is three to four years (Rhodes et al.

1998). The mean home range over a year was found to be around 1,224 hectares and the animals moved an average minimum distance of 10.3km (range 3.4-31.7km) in a 12-hour nighttime period (Rhodes et al. 1998). The researchers reported that jackals travel at approximately 1.4 km/h but can run at approximately 20 km/h (Rhodes et al. 1998). They also estimated resident, territory-holding adult population size to be 20-30/100 km2 while the total jackal population of the study area was estimated at 60-90 jackals/100 km2

(Rhodes et al. 1998). Though they did not establish an overall population density from their observations, they report that person communication with J. Bingham, an expert in the field, who was able to predict a side-striped jackal density in the area at about 1 jackal/km2 (Rhodes et al. 1998).

This information was then included in a model which serves as a good example for how to apply such data to a simple model in order to provide information about reservoir maintenance populations. The researchers in this study used a model structure based off of a model that had been used to account for rabies infection in European foxes

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(Anderson et al. 1981). The model created relied on a basic SEI framework using the

following parameters:

a = density independent birth rate b= non-disease associated death rate γ = density dependent death rate σ = mean incubation period 20 days (Foggin et al. 1988) β = contact rate 1 contact/7 days α = average life expectancy of a rabid jackal 5 days (Foggin et al. 1988) N = initial population size 400,000 K = carrying capacity of environment

The equations for each class are described below:

1.) dS/dt = αS - bS – γSN – βSI Susceptible population at a given point in time = average life expectancy of a rabid jackal x number of susceptibles - non-disease associated death rate x number of susceptibles - density dependent death rate x number of susceptibles x initial population size - contact rate x number of susceptibles x number of infected

2.) dE/dt = βSI – bE – γEN – σE Exposed population at a given point in time = contact rate x number of susceptibles x number of infected - non-disease associated death rate x number of exposed - density dependent death rate x number of exposed x initial population size – mean incubation period x number of exposed

3.) dI/dt = σE – bI – γIN – αI Infected population at a given point in time = mean incubation period x number of exposed - non-disease associated death rate x number of infected - density dependent death rate x number of infected x initial population size - average life expectancy of a rabid jackal x number of infected By summing these three equations together, the rate of change of the total jackal population is given by:

4.) dN/dt = aS - bN - γN2 - αI Number of individuals in population at a given point in time = density independent birth rate x number of susceptibles - non-disease associated death rate x initial population size - density dependent death rate x initial population size squared - average life expectancy of a rabid jackal x number of infected

Rhodes et al. (1998) emphasize that they did not incorporate information about spatial

distribution of jackal rabies cases or the rates of spread of the infection into largely

153 susceptible areas which would likely alter model results. They state that such spatial information has yet to be acquired. When running the models, they assumed that jackal cubs are usually born between August and January so they doubled the per capita birth rate for half of the year to account for the seasonal heterogeneity (Rhodes et al. 1998).

They also removed density dependent death rate (γ) during months where no cub birth was assumed (Rhodes et al. 1998). They then calculated the basic reproductive number as below:

R0 = σβK/(σ + a)(α + a) Basic reproductive umber = mean incubation period x contact rate x carrying capacity/(mean incubation rate + density independent birth rate) x ( average life expectancy of a rabid jackal + density independent birth rate)

Results showed that the minimum density required to maintain infection is around

2 1.4 jackals/km (Rhodes et al. 1998). Because this density is greater than the estimated jackal density of 1 jackal/km2, the researchers conclude that the population density is too low for jackals to act maintenance reservoirs of rabies independent of domestic dog populations (Rhodes et al. 1998). The researchers note that the observed jack density of around 1 jackal/km2 is not too far from the predicted threshold density for maintenance of

1.4 jackals/km2. As a result, they expect that whenever rabies is introduced into a jackal community the infection can persist for some time (Rhodes et al. 1998). Additionally, in circumstances where jackals can exist at densities of greater than 1.4km2, it is believed that they could maintain the disease and possibly initiate front-like rabies epidemics

(Rhodes et al. 1998).

Rhodes et al. (1998) state that in order for the jackal population to match records of the annual incidence of jackal rabies in Zimbabwe from 1950-1994, either the contact

154 rate among jackals is more frequent than estimated from the field data or there is frequent reintroduction of rabies into the jackal population from an external population (Rhodes et al. 1998). They conclude that the results are due to high between-species transmission rates between domestic dog and jackal populations (Rhodes et al. 1998). This conclusion was based off of estimates that approximately 21% of the total land area of Zimbabwe is available for dog-jackal interaction (Foggin 1988). This information was scaled to the relevant populations to get an idea of the actual numbers of animals involved in this border region. They then assumed that in the border regions, jackals contact dogs as often as they encounter other jackals (1 contact/7days) (Rhodes et al. 1998). They conclude that according to such calculations, they were able to demonstrate that the overall incidence of rabies in jackals tracks the incidence of disease in dogs suggesting that dogs act as the sole maintenance reservoir of rabies in Zimbabwe (Rhodes et al.

1998). Unfortunately this conclusion is based off of a significant amount of estimation and parameter assumptions. As mentioned, including spatial and behavioral information would most likely alter model results (Rhodes et al. 1998). The threshold density estimate of 1.4 jackals/km2 compared to 1 jackal/km2 is too close to determine whether or not independent maintenance is possible. It appears that side-striped jackal populations are on the verge of being able to maintain rabies cycles independent of domestic dogs in the study area. In areas or situations where the jackal population exceeds the domestic dog population as well as the density threshold, maintenance may be established while in areas where domestic dogs continue to dominate, the incidence of rabies in jackal will likely track incidence or rabies in domestic dogs as predicted by Rhodes et al. (1998).

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Results from this study were supported by results from a study by Bingham et al.

(1999). This study provides detailed information on domestic dog and jackal rabies outbreaks from 1950-1996 and seeks to determine whether or not either black-backed jackals or side-striped jackals are able to maintain rabies transmission cycles independent of domestic dog populations. The two jackal populations are analyzed separately here.

During the period of study, these two jackal species made up 25.2% of all confirmed rabies cases in the country, second only to domestic dog (Bingham et al. 1999). Both side-striped jackal and black-backed jackal cases occurred primarily in the commercial farming sector.

It was found that side-striped jackal rabies occurs as dense epidemics separated by periods in which the disease was unreported and probably extinct except in limited areas

(Bingham et al. 1999). These outbreaks began at a single focus and proceeded to radiate outwards (Bingham et al. 1999). In general, the foci were initiated by cases of rabid dogs but once initiated in side-striped jackals, the epidemic was able to be maintained by the side-striped jackal population alone (Bingham et al. 1999). This is consistent with what was reported by Rhodes et al. (1998). Bingham et al. (1999) also found that rabies in the side-striped jackal showed two seasonal peaks with the main peak occurring during

January-March and the second peak from July-August. The researchers believe that the

January to March peak was probably related to high densities of independent mobile juveniles while the July-August peak may have been associated with the increased contact rate between adult jackals during the breeding season (Bingham et al. 1999). The epidemics were found to terminate after expanding to geographical limits which corresponded roughly with the limits of the commercial farming sector (Bingham et al.

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1999). The researchers believe that wide communal areas, protected areas and the jackal inter-species zone interface act as barriers to the further progression of the epidemics

(Bingham et al. 1999).

In the black-backed jackal, rabies was found to occur with less variability however at lower frequencies since the early 1970’s (Bingham et al. 1999). However, the researchers state that the lower frequency of cases observed is likely due to lower reporting rates considering their distribution lies in an area with a much lower human population. Higher prevalence was observed during June to September and lower prevalence was observed in November in this species (Bingham et al. 1999). As with the second peak in the side-striped jackal, the June to September peak for black-backed jackals was thought to be associated with increased contact rate between adult jackals during the breeding season (Bingham et al. 1999). Bingham et al. (1999) state that the black-backed jackal is also able to maintain rabies transmission independent of other species, however, they state that the epidemiology is unclear. Interestingly, epidemics in black-backed jackal populations seemed to terminate once reaching the jackal interspecies zone interface (Bingham et al. 1999). The researchers note that transmission between the two jackal species did not seem to happen readily, most likely due to low inter-specific contact. They also found that among both species, young adults (about 1 year of age) appeared to suffer from rabies less frequently than jackals in other age categories (Bingham et al. 1999). Juveniles, sub-adults and adults were thought to have greater contact with one another through social affiliative or antagonistic interactions whereas young adults, which were found to have a tendency to be more independent of

157 their family groups, were less likely to have home ranges to defend and tended to have fewer social interactions (Bingham et al. 1999).

The authors conclude that domestic dogs, which had been reported to carry rabies continuously over most areas of the country since 1950, serve as long-term maintenance reservoir hosts of rabies that maintained the disease over periods when it was absent in side striped jackals and continued to re-introduce it to jackals once jackal populations again reached threshold densities (Bingham et al. 1999). Though Bingham et al. 1999 state that the data show that rabies in jackals is maintained independently of domestic dogs, their definition of maintenance seems to be different than what has previously been described. What they report here is that jackals are able to maintain intra-specific transmission once introduced from an outside source. This is consistent with the stuttering chain or potential emerging infectious disease scenarios described by Viana et al. 2014 in their continuum. This scenario still relies on repeated introductions from an outside population. Both Haydon et al. (2002) and Viana et al. (2014) state that in order for a populations to be considered a maintenance population, the pathogen must be able to persist in the population over the long term. However, it is difficult to know what the cut-off point for “long term” is. However, the scenario described by Zulu et al. (2009) where rabies cycles in western Limpopo were maintained exclusively by black-backed jackal populations over a 5-year period does seem to provide more support for maintenance than the scenario described in this study. Again, the scenario described by both Rhodes et al. 1998 and Bingham et al. 1999 in the side-striped jackal seem to show that the side-striped jackal population is on the verge of being able to maintain independent rabies cycles in certain areas and is sensitive to minor shifts in population

158 structure. The R0 seems to be either slightly above or slightly below 1 with the potential to take off once the threshold is exceeded as described by Viana et al. (2014).

A Different Perspective

A review by Bingham (2005) slightly complicates matters by re-defining

“maintenance” and “persistence,” however, his reason for doing so is valid and introduces important considerations. Bingham (2005) emphasizes the significance of the scale at which ecologic systems are examined and how scale plays a major role interpretations of epidemiological systems. He provides a comparison of how a local farmer vs. a national epidemiologist may interpret the same situation. The local farmer may perceive rabies on his property as epidemic in nature with intense outbreaks separated by long periods of absence while the national epidemiologist would claim that the disease is endemic in the country. Both observations are correct however the epidemiology is perceived differently by different observers because they view disease frequency at different scales (Bingham 2005).

Bingham (2005) believes that “maintenance” should be defined as the notion of indefinite transmission of virus through members of a host population. He states that

“indefinite” does not mean the same thing as “permanent” but rather denotes an open- ended cycle that is dependent on the availability of susceptible hosts. A maintenance host is then defined as a member of a population of susceptible individuals that can replicate, shed and transmit virus efficiently to conspecifics (Bingham 2005). Such maintenance hosts live in local populations (a set of individuals that live in the same habitat patch and therefore interact with each other) which support “indefinite” (open-ended until susceptibles run out) transmission of virus independently of other local populations.

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Therefore, individual local populations are unlikely to maintain rabies continuously because of the high instability of the disease in any single local population (Bingham

2005). Local populations must rely on reintroduced from other infected local populations

(Bingham 2005).

He believes that “persistence” should be defined as the concept of continuous, long-term presence of disease within a metapopulation in which at least one infected local population within the host metapopulation is always present (Bingham 2005).

According to Bingham, a metapopulation is a set of discrete local populations within some larger area, where migration from one local population to at least some other patches is possible. This definition encompasses the previous definition provided by

Viana et al. (2014) stating that a metapopulation is a set of populations that are connected by transmission and can be comprised of structured populations of the same species (e.g., in space), populations of different species, or a combination of the above. The only major difference is that the Viana et al. (2014) definition does not mention the scale as being a “larger area.” Therefore, the previously stated definition of a maintenance population, which refers to the ability of a disease to persist in a population over the long- term, is more similar to Bingham’s definition of persistence. His definition of maintenance would actually include what was previously defined as a non-maintenance host in which transmission cannot persist in the long-term. This definition of maintenance might include the “stuttering chains” as well as the “large outbreak” scenarios that were described in the Viana et al. (2014) continuum.

Bingham (2005) goes on to apply his definitions to rabies transmission in South

Africa. He argues that previous studies that have questioned the ability of jackals to

160 support rabies virus cycles have not distinguished between the ability of a species to support pathogen cycles (maintenance) and the concept of long-term persistence.

According to Bingham (2005), acknowledging this distinction would show that local populations of wild canine species may maintain epidemics independently and may even be rabies-free for periods of time between epidemics. He believes that at the local population level, patterns of rabies transmission are essentially similar in domestic dogs, jackals and other canids. According to Bingham (2005), it is biased to claim that domestic dogs appear to support rabies infection endemically while jackals do not, simply because there are more numerous discrete local dog populations within the study area than jackal populations. He argues with the conclusions drawn by the Rhodes et al.

(1998) study are incorrect. As mentioned above, this study that concluded that the average side-striped jackal population density in Zimbabwe was too low to maintain the chain of rabies infection and that the jackal population does not seem to be able to support rabies infection endemically without frequent reintroductions from outside sources (Rhodes et al. 1998). Bingham (2005) argues that these jackal populations should be considered capable of maintenance but not persistence. He mentions that the jackal populations of Zimbabwe should not be considered metapopulations because they do not have the spatial separation or inter-population migration (as domestic dog populations do) that would be necessary for them to be considered metapopulations. This absence of a metapopulation structure explains why rabies is unable to persist in jackal populations and this absence, rather than their inability to maintain virus transmission, distinguishes jackals from dogs as hosts of rabies (Bingham 2005). According to Bingham’s

161 definitions, the side-striped jackal would be considered capable of maintaining rabies cycles independent of other species but these cycles would not be considered persistent.

The commonly accepted definitions for reservoir and target populations are boldly challenged by Bingham (2005) as well. Haydon et al. (2002) states that a reservoir is a population in which a pathogen can be permanently maintained. Bingham (2005) claims that this definition is problematic because the term “permanently maintained” is ambiguous and according to this definition, dogs but not jackals, are considered reservoirs because they are permanent hosts. He states that many dog populations, as with many jackal populations, do not permanently support rabies cycles and that such definitions fail to provide a convincing argument that fundamental distinctions exist between dogs and jackals in their ability to support rabies cycles (Bingham 2005).

Haydon et al. (2002) also defined reservoir populations in reference to target populations which are defined as the population of concern to us. Bingham (2005) states that this term is anthropocentric and not useful for understanding the biologic mechanisms of pathogen emergence and persistence.

Outside of arguments about what should and should not be considered maintenance populations according to his definitions, Bingham (2005) mentions that one possible mechanism for rabies persistence is the concept of long incubators. He states that these individuals help establish persistence by carrying infection during calm, seemingly rabies free, periods so that they can restart epidemics once the host density has recovered

(Bingham 2005). He also considers the potential for an asymptomatic carrier state based on results from East et al. (2001) however concludes that no evidence currently shows

162 that carrier animals or long incubators play a role in the persistence of rabies cycles in canine hosts (Bingham 2005).

The review is concluded with a very important statement about where future surveillance efforts should be targeted. Bingham (2005) states that increasing human populations, urbanization and the introduction of large-scale commercial agriculture have changed the ecology of African ecosystems allowing species that can exploit these changes to flourish in the new ecological landscapes in close proximity with humans and their domestic animals. Such species are the best candidate hosts for the maintenance of zoonotic pathogens (Bingham 2005). Therefore, these species should be targeted in surveillance efforts.

Identification of Other Lyssaviruses in Zimbabwe

A re-evaluation of isolates collected between 1983 and 1997 in Zimbabwe that were initially identified as rabies positive was carried out by Bingham et al. (2001). The analysis was carried out using a panel of anti-lyssavirus nucleocapsid monoclonal antibodies. Out of 56 isolates from cats and various wild carnivore species, researchers were able to identify one isolate of Mokola virus and five other non-typical rabies viruses

(Bingham et al. 2001). The Mokola virus isolate was identified in a domestic cat and the analysis suggested that it is a distinct subgroup of the Mokola virus clade (Bingham et al.

2001). The five non-typical rabies viruses were isolated from two honey badgers

(Mellivora capensis), two African civets (Civettictis civetta) and an unidentified mongoose (Herpestidae) (Bingham et al. 2001). These isolates are thought to be representatives of rarely-reported wildlife-associated strains of rabies maintained by the slender mongoose (Galerella sanguinea) (Bingham et al. 2001).

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Interestingly, the isolates associated with the slender mongoose rabies strain were not obtained from slender mongooses suggesting that other species participate in the maintenance of this virus variant (Bingham et al. 2001). The maintenance host of Mokola virus (genotype 3) is not known however the majority of isolates have been from domestic cats. Other isolates have been found in shrews (Crocidura sp.), a rodent

Lophuromys sikapusi, a domestic dog and human beings (Bingham et al. 2001). Mokola virus appears to be unique to Africa and appears to occur throughout the continent.

Analysis with monoclonal antibodies against the nucleoprotein and phosphoprotein epitopes show that there are at least five distinct groups of Mokola virus consisting of a

West African prototype, three distinct South African variants, and an isolate from

Zimbabwe (Bingham et al. 2001). The researchers believe that this suggests separate groups within this genotype that are associated with different geographical regions. They believe that the genetic and antigenic diversity of Mokola virus in such a small geographical range of southern Africa may indicate long periods of evolution (Bingham et al. 2010). Bingham et al. (2001) conclude that their findings indicate that both Mokola virus and the mongoose-associated variant may be more common in Zimbabwe and other

African countries than is apparent from routine surveillance.

Rabies Transmission in Kenyan Carnivores

A study conducted from 2000–2009 in northern Kenya (Laikipia District and parts of neighboring Isiolo and Samburu Districts) investigated serological patterns of exposure to rabies virus as well as canine distemper virus (Prager et al. 2012). However, only rabies virus results will be discussed here. The researchers sought to discover the reservoir for both viruses. Prevalence of serum antibodies was used as a measure of past

164 exposure to investigate pathogen persistence in sympatric domestic dog, black-backed jackal, spotted hyena, striped hyena, lion and African wild dog populations. Prevalence of rabies virus shedding in saliva was also used to assess current infection status in jackals, wild dogs and both spotted and striped hyenas.

The study area consisted of 2,500 km2 of semi-arid bush land used for subsistence pastoralism, commercial ranching, and tourism. Domestic dog densities were reported to be higher on the pastoralist lands (3.4 domestic dogs/km2) (Prager et al. 2012) than on private ranches (0.21 domestic dogs/km2) (Woodroffe and Donnelly 2011). From the years 2000-2006, the majority of sampling for spotted hyena, striped hyena and jackal took place on private ranches however from 2006-2009, these species were also sampled from two sites in pastoralist areas in order to investigate differences in pathogen exposure between high and low domestic dog density areas.

A titer of 0.05 IU/ml was used as the cut-off threshold for identification of seropositive samples (Prager et al. 2012). Results found that all species sampled, with the exception of lions and striped hyenas, showed evidence of exposure to rabies virus

(Prager et al. 2001). However, only one jackal (n=69) was found to be seropositive

(Prager et al. 2012). Over the study period, seroprevlance was found to be 0.28 IU/ml in domestic dogs, 0.086 in African wild dogs, 0.065 in spotted hyena, 0.014 in black-backed jackals and 0 in striped hyena (Prager et al. 2012). The authors report significant inter- annual variation in rabies virus exposure in spotted hyenas and wild dogs while seropositivity in the domestic dog population did not vary among years (Prager et al.

2012). Additionally, they found that rabies seropositivity did not differ by sex or age class in any of the species analyzed (Prager et al. 2012). When all species were analyzed

165 together, seropositivity was found to vary significantly by species and year (P < 0.01)

(Prager et al. 2012). No rabies virus shedding was detected in any of the saliva samples and no significant differences in rabies incidence between areas with low (private ranches) and high (pastoralist areas) domestic dog densities for any wild carnivores were detected (Prager et al. 2012). This study reports that rabies was able to be maintained in the domestic dog population at a density of 3.4 domestic dogs/km2 (Prager et al. 2012).

This is much lower than the rabies threshold density estimated by both Cleaveland and

Dye (1995) of >5 dogs/km2 as well as Lembo et al. (2008) of 11 dogs/km2.

Prager et al. (2012) conclude that rabies virus persisted in the domestic dog populations inhabiting pastoralist areas of their study but did not persist in wild carnivore populations (Prager et al. 2012). They state that the observed patterns suggest that rabies virus was introduced periodically from an external reservoir or un-sampled host species, leading to short transmission chains and rapid pathogen fade-out (stuttering chain on continuum) (Prager et al. 2012). This is similar to the side-striped jackal scenario described by Rhodes et al. (1998) and Bingham et al. (1999). Prager et al. (2012) suggest that the domestic dog population in which rabies virus appeared to be persistent had the potential to function as a reservoir from which the pathogen occasionally spilled over into wild carnivores. They note that the finding that rabies virus persisted at domestic dog densities lower than the thresholds detected by other studies is a reflection of methodological differences. While Prager et al. (2012) carried out active surveillance of live dogs and wild carnivores to identify levels of non-lethal exposure to rabies virus, other studies mostly monitored deaths due to rabies virus. They mention that examination of deaths can be complicated by difficulty locating carcasses, carcasses may be lost to

166 scavengers before samples can be collected, or they may be too decomposed for retrieval of diagnostic samples resulting in missed cases due to detection bias (Prager et al. 2012).

They failed to mention that, as previously described by Viana et al. (2014), interpretation from seroprevalence surveys is often unreliable due to cross-reactivity, declining antibody titers, cut-off thresholds used to distinguish positive and negative reactions and difficulty with detectability of antibodies.

The finding that there was no difference in rabies virus exposure in wild carnivores on pastoralist lands (supporting high domestic dog densities) and private ranches (supporting lower domestic dog densities) was surprising to the researchers however, they mention that it only takes a single rabid domestic dog to cause a spillover event (Prager et al. 2012). The authors believe that differences in population structure, population size and species-specific behavior may explain why rabies virus is able to persist in domestic dog populations but not those of wild carnivores. They state that wild carnivore packs rarely encountered one another, thus introducing limited opportunities for pathogen spillover into a single pack (Prager et al. 2012). If such spillover does occur, they believe that it will tend to cause a local epizootic that fades out before transmission to another pack (Prager et al. 2012). The researchers believe that domestic dog populations have a better chance of maintaining rabies transmission because they experience frequent inter-group mixing and tend to live at higher population densities

(Prager et al. 2012).

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What is Known about Transmission Dynamics of Rabies in Ethiopian Wildlife:

Seasonality

Seasonality of rabies in Ethiopia seems to vary due to multiple interacting factors.

Reports in Addis Ababa indicate that higher numbers of confirmed rabies cases generally occur during the summer months between June and September which is the long rain season (Ali et al. 2011, Reta et al. 2014). Annual variation of rabies occurrence was observed in Addis Ababa as well (Reta et al. 2014). This variation is thought to be due to rabies control activities that were launched but not maintained continuously (Reta et al.

2014). This is further supported by a Hampson et al. (2007) study that examined annual fluctuations in rabies epidemics throughout southern and eastern Africa and concluded that a combination of superspreader dispersal and inconsistent vaccination responses seem to be the most plausible explanation for observed annual trends. They note that historical rabies data show that when control measures deteriorate, epidemics rapidly reemerge even in areas that have been rabies-free for long periods of time. This is believed to be indicative that considerable dispersal must occur from endemic rabies regions or that a small proportion of rabid dogs may act as superspreaders infecting many others or transmitting the disease over large distances (Hampson et al. 2007). In the

Oromia region (large region that spans over central, southern, and parts of western

Ethiopia), peaks in rabies occurrence were observed in April and May in both highland and lowland areas (Okell et al. 2013). This period is during the dry season. However in the highland areas, a second peak was observed in October which is thought to be related to the fact that this time period is immediately prior to harvest when household food levels are particularly low so people do not provide waste food to domestic dogs which

168 results in more scavenging behavior among the dogs (Okell et al. 2013). In the lowland areas, a second peak occurred in September, again during the long rain season, consistent with reports from Addis Ababa (Okell et al. 2013). Interestingly, reports from both highland and lowland groups indicated an overlap between times when incidence of clinical signs of rabies was highest with times when jackals were most likely to be seen and rainfall levels were low (Okell et al. 2013).

Reports from previous studies in other African countries have shown an increase in incidence of rabies cases during the dry season (Bingham and Foggin 1993, Courtin et al. 2000). This is thought to be due to increased canid movement resulting in increased contact rates as a result of food and water scarcity during the dry season (Courtin et al.

2000). This would explain the peak in cases observed during the April-May period in the

Oromia region as well as the peak in cases coinciding with increased jackal sightings.

The pre-harvest peak in October is, again, likely the result of increased canid movement due to low resource availability (Okell et al. 2013). However, the peaks observed in

Addis Ababa from June-Septemeber along with the secondary peak observed in

September in the lowland areas of the Oromia region warrant a different explanation.

These peaks occur during the long rain season when, in general, there is less resource scarcity. The observed increase in cases is thought to be a result of mating season in dogs and other species which coincides with this time period (Ali et al. 2011, Reta et al. 2014).

Mating season results in higher contact rates and increased movement as males search for and fight over females (Ali et al. 2011). In other countries, such as Namibia, canid species such as the black-backed jackal tend to mate during the dry season months

(Courtin et al. 2000). Because mating coincides with the dry season in these countries, it

169 would be difficult to distinguish the effects of increased contact rates due to resource scarcity or due to mating season on rabies virus transmission.

Summary of The Economy and Environment

Ethiopia is one of Africa’s largest countries, with an area of approximately 1.1 million km2 (United Nations Statistics Division 2016). The human population is estimated to be nearly 102 million (101,853,000) with a density of 101.9 people/km2

(United Nations Statistics Division 2016). Just within Addis Ababa, the capital city, alone there are over 3 million residents (3,238,000) (United Nations Statistics Division 2016).

Agriculture is the main source of income for the country making up 72.7% of employed people in the country (unemployment is 5.2%) and accounting for 41.9% of the country’s

Gross Value Added (United Nations Statistics Division 2016). In 1998 it was reported that the livestock sub-sector included some 28 million cattle, 23 million sheep, 17 million goats, 7 million equines and 1 million camels (Zewde 1998). Considering that this was almost 10 years ago it can be assumed that the numbers have grown substantially. The majority of the country is made up of highlands rising up to 13,000 ft. but dropping sharply to the Sudan border in the west and towards the Denakil lowlands in the north- east (Zewde 1998). The land falls more gently towards Kenya in the south and the

Ogaden desert and Somalia in the east. The highlands are divided by the northern end of the Rift Valley, which forms a series of lakes. The climate is temperate on the plateau but hot in the lowlands (Zewde 1998). Forested areas used to make up a significant portion of the land cover but now only account for between 11%- 12.4% of the area (Critical

Ecosystem Partnership Fund 2011, United Nations Statistics Division 2016). The human- wildlife-domestic animal interface is constantly growing as intense pressure from

170 expanding agriculture is placed on remaining natural ecosystems (Randall et al. 2006,

Johnson et al. 2010). Ethiopia has been identified as one of the countries with the highest relative risks for outbreaks caused by zoonotic pathogens originating in wildlife based on a predictive model of future emerging infectious disease (EID) events based on 335 previous EID events (Jones et al. 2008). More details about the economy and environment in Ethiopia, with specifics about the Eastern Afromontane Biodiversity

Hotspot, can be found in Chapter 3 section 2 “Ethiopia as a Hotspot.”

Rabies Transmission in Domestic Dog Populations

The first major outbreaks in Ethiopian dog populations were reported in 1884 in northern Ethiopia (Fekadu , 1982). Rabies is now endemic in Ethiopian dog populations which are currently considered to be the primary reservoirs of rabies in Ethiopia (Deresa et al. 2010, Johnson et al. 2010). The dog population in Ethiopia has been expanding significantly along with the human population, well past the estimated threshold density for persistence of rabies virus in domestic dogs which is between 5 and 11 dogs/km2

(Cleaveland and Dye 1995, Lembo et al. 2008). The Ministry of Agriculture Veterinary

Services Annual report for the year 2000 estimated that the dog population in Addis

Ababa was between 150,000-200,000 of which only 50% were household dogs and the rest were stray dogs (Deressa et al. 2010). By 2011 the dog population in the city was estimated to be between 250,000 and 350,000 dogs (Reta et al. 2014). Though no hard data on dog population and ecology is available, estimates from 1998 suggested that the dog/human ratio was 1:6 in urban areas and 1:8 in rural areas (Zewde 1998). Estimates from 2010 indicated that there was roughly one owned dog per five households at a national level (Deressa et al. 2010). A study in the Awash Basin of Ethiopia in 2016

171 found that the dog/human ratio was 1:4.7 and reported that most households in Ethiopia, especially in rural areas, own at least one dog to protect themselves, their crops, and their livestock (Tschopp et al. 2016). They also found that dogs were owned in 33% of urban and 75.5% of pastoralist households respectively (Tschopp et al. 2016). Though there is most likely variation in dog population density between urban and rural areas, it appears that the dog/human ratio is between 1:4.7 and 1:8 according to these estimates.

The majority of these household dogs are free-roaming, having the ability to go out and interact with wildlife and then return to the home sharing whatever pathogens they may have acquired with their families. A study in the Bale Mountains National Park estimated that the average home range of family dogs is 4.345 km2 however some individuals were found to have home ranges as great as 20.6km2 (Atickem et al. 2010).

One of the greatest challenges faced when combating dog rabies in Ethiopia is the large population of stray or community-owned free-roaming dogs that go unvaccinated. Stray dogs that are not accessible to mass vaccination and can reduce achievement of vaccination coverage goals (Deressa et al. 2010). Studies on dog ownership pattern and awareness of rabies in Addis Ababa showed that 90.7% of dog owners manage dogs for the safeguarding of their properties from theft out of which 52% were unvaccination

(Deressa et al. 2010). A similar study in Addis Ababa found 56.4% of owned dogs were unvaccinated (Ali et al. 2014) while a study in the more rural area of Jimma Town found that 95.2% of owned dogs were not vaccinated (Kabeta et al. 2015). During the period from 2008 to 2011, 87.19% of suspect rabid dogs in and around Addis Ababa were confirmed to be rabid. The proportion of rabid female dogs (87.5%) was higher than that of males (73.44%) and dogs 3 to 12 months old were diagnosed with rabies more

172 frequently (76.6%) than dogs belonging to other age categories (Reta et al. 2014).

Interestingly, a study that examined rabies samples in and around Addis Ababa from

2003-2009 found that there was a higher proportion of rabid males (79.2%) than females

(66.9%), so it appears that the proportion has shifted in more recent years (Ali et al.

2011). The majority of rabies cases from 2008 to 2011 were diagnosed in dogs whose ownership was not known or which were ownerless (Reta et al. 2014). The rate of rabies virus exposure in stray dogs was found to be 2.27 times higher than owned dogs

(OR=2.27) in samples examined in and around Addis Ababa from 2003-2009 (Ali et al.

2011).

Reta et al. 2014 mention that in their 2008-2011 study in Addis Ababa, public awareness by using television, radio and pamphlets to strengthen control activities were emphasized. They state that though these activities together with the expanding construction in the city were able to reduce the number of dogs in the city, dogs still remain to be the main vector of rabies.

Rabies Transmission in the Ethiopian Wolf

The Ethiopian wolf is the world’s rarest canid, with an estimated population of fewer than 500 individuals limited to only seven isolated Afro-alpine ranges across the

Ethiopian highlands, all of which are under intense pressure from expanding agriculture

(Randall et al. 2006, Johnson et al. 2010). Rabies poses the most immediate threat to their survival, causing epizootic cycles of mass mortality (Martson et al. 2015). Extensive perrenteral vaccination efforts have been carried out in domestic dog populations surrounding Ethiopian wolf territory yet repeated outbreaks continue to occur (Sillero-

Zubiri et al. 2016). In each of three major outbreaks of rabies from the years 1990-2009

173 in the Bale Mountains where the majority of remaining wolves live, up to half of the existing wolf population was eliminated resulting in a long-term negative population growth rate (Marino et al. 2011). It is suspected that smaller outbreaks may have occurred undetected in other, less-well studies Ethiopian Wolf populations in areas such as the

Simien Mountains (Marino et al. 2011). Yet another outbreak occurred in 2015 again in the Bale Mountains National Park (Deressa et al. 2016).

Ethiopian wolves occur naturally at high densities (about 1 wolf/km2) (Sillero-

Zubiri et al. 1996). Though they have solitary foraging habits, generally preying off of rodent fauna, competition for limited habitat often causes delayed dispersal of males who tend to remain in the natal pack as a result. This leads to the formation of close-kin groups of up to 13 adults (Randall et al. 2006). All pack members cooperate in raising the offspring (2-7 pups) born annually to the dominant female (Randall et al. 2006). Packs defend discrete areas of habitat thus maintaining a tight mosaic of pack territories that occupy all suitable habitat (Randall et al. 2006). The combination of high wolf population densities, high contact rates between and within packs and the presence of sympatric domestic dogs all increase the likelihood and severity of disease epizootics (Sillero-

Zubiri et al. 1996).

In parts of the Bale Mountains, the wolves are forced to coexist with humans, livestock, and domestic dogs. Opportunities for sylvatic and urban rabies cycles to overlap are introduced in this setting where domestic dog and wild animal species share and compete for the same resources (Deressa et al. 2016). Dog movement within Bale

Mountain National Park is not restricted which allows the potential for a single rabid dog scavenging in the park to easily transmit the virus to Ethiopian wolves and vice versa

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(Deressa et al. 2016). Rodents, such as mongoose, may also play a role in transmission however the status of rabies in these animals is currently unknown (Deressa et al. 2016).

Observational studies have shown that wolves normally avoid direct contact with dogs however when interactions do occur, dogs dominated the wolves and chased them

(Sillero-Zubiri et al. 1996). The wolves were also observed to interact with serval cats

(Felis sertal), spotted hyenas (Crocuta crocuta), and honey badgers (Mellivora capensis)

(Sillero-Zubiri et al. 1996).

Domestic dogs also serve as a threat to Ethiopian Wolf conservation by interbreeding. Not only does hybridization interfere with preserving the genetics of the species, but outbreeding depression can also occur in which offspring have reduced fitness compared to either parent population (Siller-Zubiri et al. 1996, IUCN/SCC Canid

Specialist Group 2011). Interestingly however, it appears that dog-wolf hybrids are more resilient to rabies infection and are more likely to survive rabies outbreaks than non- hybrid Ethiopian wolves (Sillero-Zubiri et al. 1996).

Both monoclonal antibody testing and phylogenetic analysis have shown that rabies is not endemic in the Ethiopian wolf population but rather, outbreaks likely occur as a result of spillover of rabies viruses endemic in domestic dogs in Ethiopia of the

Africa 1a lineage (Sillero-Zubiri et al. 1996, Johnson et al. 2010, Marston et al. 2015).

Previous attempts to eliminate rabies in the Ethiopian wolf population have focused on vaccinating the local domestic dog population. After one of the major outbreaks (2003), the direct vaccination of wolves was permitted to control the spread of infection (Cleaveland et al. 2007b). Despite these attempts, outbreaks still continued to occur. Most recently, attempts at oral vaccination have been made. Sillero-Zubiri et al.

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(2016) carried out field trials of the oral vaccine SAG2, a live attenuated virus, in the

Ethiopian wolf population of the Bale Mountains National Park. Their results confirm the potential for SAG2 , if used with goat meat bait, to effectively protect Ethiopian wolves against rabies (Sillero-Zubiri et al. 2016). There were some issues with wolves not puncturing the sachet and just swallowing it whole thus rendering it ineffective.

However, out of 21 wolves that were able to be trapped after vaccination, 14 were positive for the biomarker. Of these, 86% had titers considered sufficient to provide protective immunity to wildlife (P0.20 IU/ml) and 50% had antibody titers above the universally accepted threshold in humans of P0.5 IU/ml (Sillero-Zubiri et al. 2016). The authors conclude that this method offers the most cost-efficient, safe and proactive approach to protect Ethiopian wolves from the rabies virus (Sillero-Zubiri et al. 2016).

Randall et al. (2006) stated that even low oral vaccination coverage of 20–40% by this method should dramatically improve population persistence (Randall et al. 2006).

In order to fully understand the details of transmission events resulting in rabies outbreaks in the Ethiopian wolf population, further phylogenetic studies including samples from all potential rabies vectors in Ethiopia should be carried out. Such investigations have not been conducted in this region of Africa but would be beneficial in attempting to control rabies within both wild and domestic animal populations (Johnson et al. 2010). Additoinally, dog vaccination should be extended and other potential reservoirs of rabies virus, particularly golden jackals (Canis aureus), should also be considered in future vaccination strategies in order to protect the Ethiopian wolf from future rabies outbreaks (Johnson et al. 2010).

Rabies Transmission in Spotted Hyena (Crocuta crocuta) Populations

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The spotted hyena (Crocuta crocuta) is the most abundant large carnivore in

Africa (IUCN Red List Least Concern) occurring in many countries including Ethiopia

(Yirga et al. 2011). Density estimates in Ethiopia show that there are roughly 52-54 hyena/100 km2 in most parts of the country (Yirga et al. 2013, Yirga et al. 2015a). This species lives in a large range of habitats including semi-desert, savanna and open woodland, dense dry woodland and in montane habitats (Yirga et al. 2013). They are crepuscular nocturnal hunters and scavengers, feeding on carrion as well as a diverse range of live prey (Yirga et al. 2013).

As mentioned in Chapter 7 section 4, Hofer and East (1995) found that spotted hyenas of the Serengeti live in large social groups, or clans, with a mean number of 45 adults and subadults at a density of 0.8 adults and subadults per km2. These groups consist of linear female and male dominance hierarchies in defended territories with female philopatry (tendency to stay in one place) and male dispersal (Hofer and East

1995). They live in matriarchal communities with adult females possessing the highest social status, and they breed year-round with cubs reared in a communal den inside the clan (Hofer and East 1995). During 46%-62% of the year, all clan members other than den-bound cubs regularly travel on average about 40 km from their territory to forage in areas containing large migratory herds (Hofer and East 1995). Territory size is roughly

5km2 on average (Hofer and East 1995). East et al. (2001) also found that oral contact rates made up about 73 ± 5 min for more than 100 hours observed at a communal den and that overall, cubs and immigrant males had significantly lower oral contact rates than adult females (East et al. 2001). This result indicates that transmission rates among adult females may be the highest making them higher risk individuals. This is supported by

177 seroprevalence studies which showed that females had the highest exposure patterns along with the highest rabies virus titers (East et al. 2001). Additionally, rates at which adult males and females received bite wounds from conspecifics increased with social status and were similar for both sexes (East et al. 2001). The R0 value was calculated to be around 1.9 but this is only considered to be a rough estimate (East et al. 2001).

Throughout Africa, there is generally little tolerance among local communities toward large carnivores due to livestock predation. Retaliation after livestock attacks is common and as a result, many of these large carnivore populations avoid villages and urban areas (Yirga et al. 2013). However, the natural prey base for carnivores is highly depleted due to habitat degradation and fragmentation (Yirga et al. 2013). This has resulted in the die off of many large carnivore populations throughout Africa. Spotted hyenas are unique in that they have a remarkable behavioral and ecological plasticity that has enabled them to survive in environments from which other large predators have been eliminated (Yirga et al. 2015a). It has been found that spotted hyena populations are able to survive almost entirely off of anthropogenic food sources and are able to peacefully co-exist with dense human populations (Yirga et al. 2013).

The diet of spotted hyenas was assessed in three sub-districts in northern Ethiopia by fecal analysis and showed that 99% of prey items were of domestic origin (Yirga et al.

2013). Only three of two-hundred and eleven fecal samples contained hair from wild animals (Ethiopian hare and porcupine). The results demonstrate the high adaptability of spotted hyenas to rely largely on anthropogenic food scavenging on skin, bones and other organic waste in addition to consuming livestock that have died from disease and drought

(Yirga et al. 2015a). Spotted hyenas have been known to feed on anthrax-infested

178 carcasses without any detrimental consequences and are able to digest every part of an animal except hair and hooves. Additionally, they are able to digest 18 kg food in 1 h

(Smith and Holekamp 2010). Studies have shown that they show a preference for scavenging at waste dumps, where slaughterhouses often discard meat scraps, as opposed to agricultural areas (Yirga et al. 2015a). Even spotted hyenas from national parks have been found to feed predominantly on anthropogenic waste (Yirga et al. 2015b). The spotted hyena population in Ethiopia has become so reliant on anthropogenic sources of food that they even experience a complete shift in diet during religious fasting periods

(Yirga et al. 2012). During fasting periods when there is no meat consumption, the hyenas have been found to change their feeding habits from scavenging to actively preying on livestock, particularly donkeys because donkeys are highly abundant and an easy target (Yirga et al. 2012).

Though spotted hyenas live close to human communities and livestock raiding is still a significant problem in some areas, there are rarely reports of retaliatory killing

(Yirga et al. 2013). This demonstrates a rare case of coexistence, where spotted hyenas benefit from waste disposal and human communities benefit from the waste clearing service by spotted hyenas (Yirga et al. 2013). In Afro-montane forests that are often found surrounding Ethiopian Orthodox monastaries (about 50m radius), hyenas are even protected and known as “God’s guards” (Yirga et al. 2011).

Domestic –Wildlife Species Interactions

There are many opportunities for contact between domestic dogs and wildlife due to competition for food and water, high density and mobility of dogs, and crop - livestock raiding by wildlife among other factors. Water points, grazing areas, slaughterhouses, and

179 waste disposal facilities are just a few examples of where interactions may occur. Such interactions facilitate the circulation of rabies virus in the country (Deressa et al. 2010).

Lack of information on the frequency of disease transmission in these settings represents a critical gap in knowledge that must be filled in order to successfully achieve elimination and prevention of rabies and other zoonoses, especially considering the high frequency with which farmers and the public report interactions between domestic dogs or livestock and local wildlife (Mojo et al. 2014).

In a study in that took place in Cheha, Ethiopia (about 180km from the capital

Addis Ababa located in the center of the country), one-hundred randomly selected households were asked to identify any wild or domestic animals that cause damage to their crops or livestock. Results show that Grivet monkeys (Cercopithecus aethiops), crested porcupines (Hystrix cristata), baboons (Papio spp.), antelopes (Gazella spp.), warthogs (Phacochoerus sp.), and wild pigs (Sus sp.) were the major crop raiders in the area, while spotted hyenas (Crocuta crocuta), foxes (Vulpes sp.), eagles (Accipitridae) and Ethiopian ratels or honey badgers (Mellivora capensis) were the most common livestock predators (Mojo et al. 2014). More than 90% of the households reported that they faced damages to their property by these species and 88% of farmers reported believing that wild animals significantly contributed to the shortages of food for their family (Mojo et al. 2014). When asked about management methods, many reported using watchdogs to keep wildlife raiders away (Mojo et al. 2014).

A study that investigated spotted hyena predation on livestock and economic impact in 10 randomly selected sub-districts throughout Oromia, Tigray, Afar, Amhara and Southern Nation Nationalities Peoples’ Regional State using semi-structured

180 interviews with 3,080 randomly selected households was conducted in 2010 (Yirga et al.

2015b). Households reported losses of 2,230 domestic animals (3.9% of their stock) or an average annual financial loss of US$10.3 per household over the 2005-2010 period

(Yirga et al. 2015b). This predation represented an estimated financial loss of $157,474

USD over five years or an annual mean loss of $31,497 USD (Yirga et al. 2015b).

Ethiopia’s average income is less than $1 per person per day (Yirga et al. 2015b).

Through scat analysis, this study also found that remains of cattle, sheep, donkey and goat were highest in decreasing order (Yirga et al. 2015b). This study was preceded by two similar, but smaller-scale, studies in northern Ethiopia that found very similar results

(Yirga et al. 2013, Yirga 2015a).

Interestingly, the Yirga et al 2015a study found that 170 dogs were victims of predation by spotted hyenas. They state that it is likely that there were far more attacks on stray dogs considering about 70% of dogs in the study areas had no owner and would not be reported in depredation surveys. In most of the reported cases spotted hyenas were observed to actively chase and kill domestic dogs (Yirga et al. 2015b). The researchers conclude that in their study, domestic dogs were clearly not effective at protecting villages from hyena attacks (Yirga et al. 2015b).

Craft et al. (2016) asked 512 villagers residing around a conservation area in the

Serengeti ecosystem, Tanzania, to report on the presence of wild carnivores in their village, the number of domestic dogs and cats in their household and interactions between domestic and wild carnivores. Though not specific to Ethiopia, this study does provide valuable information on what domestic animal-wildlife interactions are occurring and what factors contribute to interaction. In general they found that more bat-eared fox,

181 cheetah, diurnal mongoose, jackal, serval, wildcat and wild dog were observed during the day than at night whereas honey badger, leopard, lion, spotted hyena, striped hyena and white-tailed mongoose were observed more frequently at night (Craft et al. 2016). Genet were observed equally during the day and night (Craft et al. 2016). Results had a tendency to be specific to geographical regions and the types of communities occupying them within the Serengeti ecosystem. Carnivore richness was reported to be higher in pastoralist communities residing in eastern study sites while slightly less carnivore richness was reported in the western study sites made up of agropastoralist communities

(Craft et al. 2016). At eastern sites, 43% of respondents reported daily indirect interactions between domestic dogs and spotted hyenas, 28% reported weekly indirect contacts between leopards and domestic dogs, 13% observed monthly direct contacts between domestic dogs and diurnal mongoose species and 10% observed monthly direct contacts between domestic dogs and bat-eared foxes (Craft et al. 2016). At western sites,

12% of respondents reported weekly direct contacts between domestic dogs and diurnal mongoose species, 21% reported weekly indirect contacts between domestic dogs and spotted hyenas, 80% observed monthly indirect contacts between domestic dogs and white-tailed mongooses and 8% observed monthly indirect contact between domestic dogs and wildcat species (Craft et al. 2016). Twenty-five percent of questioned villagers reported losing at least one dog to hyena or leopard depredation. Overall, it appears that the majority of domestic dog-wildlife interactions occur between mongoose species and spotted hyena (Craft et al. 2016).

Human behaviors were found to play a significant role in the frequency of domestic dog-wildlife interactions. It was found that feeding leftovers from slaughtered

182 animals to domestic dogs was associated with an increase in spotted hyena sightings and a decrease in the reports of servals and wildcats (Craft et al. 2016). Sixty-nine percent of respondents reported putting their deceased domestic dog or cat carcasses ‘into the bush’ instead of burying or burning them (Craft et al. 2016). As a result, the most commonly reported scavengers found feeding on the carcasses were hyenas (88%) and carnivorous birds with <4% of respondents naming other species (Craft et al. 2016). Slaughtering of domestic animals at a central slaughter slab introduced opportunities for sequential sharing of food between domestic and wild animals (as opposed to feeding at the same time). Twelve percent of questioned villagers reported taking their livestock to a slaughter slab for killing and of these, 35% claimed that spotted hyenas visited the slaughter slab whereas 3% reported jackal (Craft et al. 2016). In a similar and related study that took place in the same area, it was found that just within this area from the years 2002-2006, there were 23 reports of bites to humans by jackal species, 20 by spotted hyena, and 17 by honey badger (Lembo et al. 2008). Craft et al. (2016) caution that using measures of geographical overlap as an indicator for direct interactions or pathogen transmission risk can result in overestimation of the importance of some wildlife species while underestimating the importance of others due to the strong influence of both animal and human behavior patterns. Knowledge of what interactions are occurring between what domestic and wildlife species and the frequency with which they occur is crucial information to understanding transmission of rabies as well as other pathogens.

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Molecular Epidemiology of the Rabies Virus in Ethiopia

A study conducted in 1992 analyzed 115 isolates of rabies viruses recovered from dogs (102), cats (5), cattle (5), and donkey, goat, and hyena (1 each) in Ethiopia by tissue culture technique (Mebatsion et al. 1992). Samples had been submitted to the public health laboratory in Addis Ababa for testing. Analysis was carried out using 17 selected antinucleocapsid monoclonal antibodies (MAbs). They found that 113 isolates were classic street rabies viruses (serotype 1) however one feline isolate was identified as

Mokola virus (serotype 3) and one domestic dog isolate was identified as Lagos bat virus

(serotype 2) (Mebatsion et al. 1992). The identification of Mokola and Lagos bat viruses in domestic animals is a significant public health and veterinary concern due to lack of effective vaccines against these agents and the difficulty of proper diagnosis.

On the basis of nucleocapsid analysis with MAbs, 107 of 115 rabies virus isolates showed an identical reactivity pattern suggesting similarity in their nucleocapsid composition (Mebatstion et al. 1992). This makes sense considering most samples were collected from Addis Ababa and isolates tend to cluster geographically (Lembo et al.

2007). A difference in reactivity pattern was observed in six isolates (excluding the

Mokola and Lagos virus isolates). This was thought to be associated with geographical location as well because though several of the six isolates came from Addis Ababa, others came from areas as distant as 600 km away (Gondar)(Mebatsion et al. 1992). The researchers note that they were surprised that the isolate obtained from the hyena showed a reactivity pattern identical to that of the other 106 isolates from domestic animals. They believe this suggests that the same virus strains adapted to dogs is circulating within wild animals (Mebatsion et al. 1992). They note that hyenas are popular scavengers in

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Ethiopia with constant movements in and out of large cities and towns during the night.

As a result, they come in close contact with most of the dogs guarding households and thus serve as vehicles and potential reservoirs for rabies transmission from rural to urban areas and vice versa (Mebatsion et al. 1992).

A more recent study examined the molecular epidemiology of rabies viruses based on nucleotide sequence analysis of ten isolates originating from three selected

Regional States of Ethiopia (Tigray, Amhara and Oromia) between 2013 and 2014

(Deressa et al. 2015). Isolates were obtained from two cattle, six dogs and two cats, all of which tested positive by DFAT (Deressa et al. 2015). The nucleotide sequences of these samples were studied in order to obtain an understanding of the epidemiological relationships of the rabies virus isolates in the various Ethiopian geographical locations.

Results showed that all ten Ethiopian rabies virus isolates belonged to genotype 1

African lineage 1a and most of the samples formed a monophyletic cluster with Sudan rabies virus isolates together with the isolates from other African countries surrounding

Ethiopia (Deressa et al. 2015). This cluster is further divided into two groups, an Ethiopia subgroup and an Ethiopia/Sudan subgroup) (Deressa et al. 2015). Previous studies have also shown that isolates from Ethiopia and Sudan cluster together suggesting a common origin (Johnson et al. 2004). The Ethiopian rabies virus isolates had an average nucleotide similarity of 95% (Deressa et al. 2015). However, in the phylogenetic analyses, one strain from a cow was located in a different position of the Africa 1a cluster

(Deressa et al. 2015). The other nine Ethiopian sequences were phylogenetically closer to the Sudanese rabies viruses. The researchers hypothesize that the strain located in a different position of the Africa 1a cluster was introduced by a high grade dairy cow

185 coming from European countries during the importation of exotic breeds (Deressa et al.

2015).

Deressa et al. (2015) note that the samples investigated in their study were not distributed evenly across Ethiopia and instead originated in close proximity to the rabies referral laboratory. They attribute this sample bias to limited infrastructure as well as limited capabilities to appropriately collect, store and ship samples in most parts of the country. Deressa et al. (2015) also emphasize that future analysis with domestic dog rabies virus, other domestic animal species and wild animal species from eastern and southern parts of Ethiopia will further enhance understanding of the cross- species transmission events that lead to epizootic cycles observed in the domestic and wild animals population. They note that of the total animal rabies cases reported from 2013 and 2014, only six were in wild animals suggesting the need for more enhanced surveillance in wildlife. Information on the molecular epidemiology of rabies in Ethiopia is lacking and there is great demand for future studies in this area. Very little can be concluded with such limited sequence data.

Methods: How to Find out More About Contact Rates

It is evident that wildlife-domestic animal interactions occur in Ethiopia at a relatively high frequency however, in order to understand rabies transmission at this interface, contact rates need to be quantified. Identifying contact rates between wildlife and domestic animals in the absence of an outbreak scenario presents many challenges and, as previously mentioned, has rarely been attempted for natural populations due to the temporal and spatial resolution at which this epidemiological data must be collected

(Real and Biek 2007). More information about general methods to obtain disease

186 transmission data from wildlife can be found in Chapter 5 section 7 “Obtaining Modeling

Data from Wildlife.”

Camera traps and animal borne camera studies are only just now beginning to be applied to studies of animal behavior and disease ecology. They may be able to provide the quantitative data needed to use as parameters in disease models. In a study carried out by Loyd et al. (2013), video systems that record an animal-eye view of activities without disrupting behavior were attached to 55 roaming cats to identify the frequency of domestic cat interactions with native wildlife, common prey species and predictors of outdoor behavior. The researchers were able to obtain 38 hours of footage from each cat and found that 44% of cats hunted wildlife of which the most common prey species were reptiles, mammals and invertebrates (Loyd et al. 2013). They were also able to obtain information on how many prey species could be captured in a given amount of time and what time of year wildlife captures occurred (Loyd et al. 2013). This information on what species are interacting and the frequency with which they interact is invaluable to studies of disease transmission. This camera data has the ability to produce the high-resolution data that is required for disease models.

Few camera trap studies have been performed to identify contact rates at this point however they have been used widely to estimate population density which is a critical component of disease transmission. Camera traps are general low labor cost, noninvasive, robust to variation in ground conditions and climate, can be used to gain information from highly cryptic species and are equally efficient at collecting data by day and night (Rowcliffe et al. 2008). Previous methods have relied on individual recognition in order to apply capture- recapture analysis from camera trap data. A variety of new

187 mathematical models have been applied to camera trap data in order to produce reliable density estimates without the need for individual recognition (O’Connell et al. 2011).

Rowclliffe et al. (2008) were able to create accurate density estimates by modeling the underlying detection process and applying the ideal gas law to camera trap data. The model creates a factor that linearly scales trapping rate with density depending on two key biological variables (average animal group size and day range) and two characteristics of the camera sensor (distance and angle within which it detects animals)

(Rowcliffe et al. 2008). When tested in a wild animal park in England with known abundances of four species, this method produced accurate estimates in three out of four cases (Rowcliffe et al. 2008). The researchers state that inaccuracy in the fourth species was because of biased placement of cameras with respect to the distribution of this species (Rowcliffe et al. 2008). They also found that precision of their estimates increased rapidly up to around 20 camera placements and continued to improve much more slowly after that.

Cusack et al. (2015) referred to the Rowcliffe et al. (2008) model as the “random encounters model” and applied it to previously collected camera trap data from the

Serengeti National Park, Tanzania, to estimate the density of female lions. They compared estimates to reference values derived from pride census data. They then applied camera trap data to the following equation in order to produce density estimates:

D= (y/t) * (π/V*r(2 + θ)

y = number of independent photographic events t = total camera survey effort V = average speed of animal movement r = radius of the camera trap detection zone θ = angle of the camera trap detection zone

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In order to estimate camera radius, the researchers approached a test camera directly from the front on all fours ten different times and then measured the distance from the camera to the location at first trigger for each approach (Cusack et al. 2015). For camera angle, they carried out ten paired approaches (one from each side) perpendicular to the sensor beam at a distance of 5m and recorded the location at first trigger (Cusack et al. 2015).

For each resulting location, they then took a bearing using a compass placed on a flat surface directly below the camera and recorded detection angle as the angle formed by the mean compass bearings taken on each side (Cusack et al. 2015). The average speed of animal movement came from four-day continuous follows of individual Serengeti prides carried out between September 1984 and December 1987 (Scheel and Packer 1991).

One of the key assumptions of this model is that cameras must be placed randomly with respect to animal movement (Rowcliffe et al. 2013). Cusack et al. (2015) note that because the camera trap studies were initially carried out without the intention of using the random encounters model, the placement of some of the cameras violated this assumption. Though cameras were placed on a grid, many of the cameras were placed on trees. Trees represent an important source of shade for the lions in a largely open savannah habitat thus, lions are drawn to these areas. This violates the random placement assumption. However, the researchers used prior knowledge of lion behavior to hypothesize that this violation would be less severe when tree cover is used less disproportionately by lions, specifically during the night, during the wet season and in woodland habitat. They found that when they excluded daytime records, their data was able to generate estimates that were not significantly different from the reference

189 population sizes (Cusack et al. 2015). Cusack et al. (2015) conclude that this is an effective method that may be used to estimate the density of a territorial and unmarked large carnivore from camera trap data on the condition that any clear violations of the model’s key assumption are identified and reduced as much as possible using prior knowledge of animal behavior (e.g. time, location).

Both animal-borne cameras and camera trap methods can be applied to produce estimates of contact rates between domestic animals and wildlife species. They can provide information on what interactions are occurring, how frequently they occur, and what behaviors are being exhibited. The major limitation will be identifying enough contacts to base conclusions off of, however, with enough sampling effort, it is probable that reliable estimates can be obtained.

Proposed Scenarios for Rabies Transmission in Ethiopia

Based off of current evidence in Ethiopia, which is very limited, it appears that if humans are considered to be the target population, then the rabies reservoir is composed of the maintenance domestic dog population and a non-maintenance spillover population of wild carnivores. Transmission occurs from domestic dogs to human and wildlife populations and from wildlife populations to human and domestic dog populations. This is consistent with what was discovered in the Serengeti ecosystem by Cleaveland and

Dye (1995) and Lembo et al. (2008) (Scenario “B” in the Lembo et al. 2008 diagram).

Cleaveland and Dye mention that though dogs are the sole reservoir of rabies virus transmission in the Serengeti District, the fact that bat-eared foxes in South Africa are capable of maintaining rabies independently of dogs in addition to findings of recurrent cases in the Serengeti highlight the need for further investigation of their role. They also

190 state that the predominance of domestic dogs among confirmed and reported cases in the

Serengeti as well as other African countries may be an artefact of less intense surveillance and that measures need to be enhanced before definite conclusions can be made regarding wildlife reservoirs in the Serengeti. It is evident that certain wildlife species in Ethiopia interact with domestic dogs somewhat frequently and the density of certain urban wildlife species, such as the spotted hyena, exist at high densities (52-54 hyena/100km2) (Yirga et al. 2013, Yirga et al. 2015a). Population estimates of other wildlife species should be a priority considering there are none that exist. Such evidence makes it is quite probable that independent sylvatic strains either exist in some regions of

Ethiopia and have yet to be identified, or are in the early stages of divergence from the domestic dog strain, similar to what was found to be occurring in bat-eared fox and jackal populations in South Africa by Nel et al. (1993a), Sabeta et al. (2003), Sabeta et al.

(2007) and Zulu et al. (2009).

All of these studies demonstrate the beginnings of new lineages diverging based on host species and geographical location. Both the cytoplasmic domain of the glycoprotein gene (long-term evolutionary trends) and the non-coding G-L intergenic region (recent evolutionary trends) were used for sequencing in all of these studies which allowed for identification of both long-term and recent evolutionary trends. If enough sample data is able to be obtained from Ethiopia as has been obtained in South Africa, it is likely that very similar trends will be observed. There is also the possibility of independent mongoose variants circulating in Ethiopia that are long removed from the domestic dog variant however there is no data available on mongoose rabies in Ethiopia.

Considering the large territorial habitat of the mongoose, covering around 75% of the

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African continent however, the potential for undiscovered cycles of mongoose rabies north of southern Africa may be significant (Hanlon 2013). With the available data it appears that wildlife rabies transmission in Ethiopia is consistent with the stuttering chain scenario described in Viana et al. (2014) continuum. Both within-and-between- species transmission appear to be high enough to cause epizootics but not high enough to establish an enzootic state. However, if only within-species transmission increases in one of the carnivore species, then the potential for a massive outbreak is introduced (potential emerging infectious disease) followed by the possibility of an enzootic state. If both within-and-between- species transmission increase in any of these carnivore species, then it is likely that that species will be able to maintain rabies in an enzootic state independent of interactions with domestic dogs. Different parts of the country may fit into these different parts of the continuum. No matter what the situation, the longer the amount of time that wildlife species continue to become infected with the rabies virus, the greater the opportunity the virus has to evolve within these species and continue any patterns of divergence from the initial canid rabies variant. Though the role of wildlife as a reservoir of rabies is not yet known in Ethiopia, it is known that domestic dogs serve as a maintenance reservoir. As a result, the faster rabies can be eliminated in domestic dogs the less likely new strains are to emerge from the original canid strain.

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Chapter 3: Molecular Epidemiology of Rabies in Ethiopia

Abstract

Ethiopia is one of the most severely rabies-affected countries on the African continent, and little information exists regarding the genetic diversity of rabies viruses

(RABV) circulating in dogs or the occurrence of alternative rabies cycles maintained by wildlife. This study encompassed 230 samples obtained from wild and domestic animals collected throughout Ethiopia during the period 2010-2017. We sequenced partial nucleoprotein genes from 187/230 samples, and obtained complete sequences for 43/230 samples. We compared sequences against references representing current RABV variants across Africa. Results identified a complex assemblage of co-circulating dog RABV variants throughout Ethiopia with detection of geographical pockets, suggesting multiple historic dissemination events across regions from an epicenter in Oromia. There was no evidence of dog-maintained rabies imported from other African countries. A RABV variant apparently established in side-striped jackals was also detected. This investigation provided a necessary baseline to monitor progress on control and elimination efforts in

Ethiopia.

Introduction

The rabies virus (RABV), classified within the family Rhabdoviridae and genus

Lyssavirus represents a major global health threat due to a high diversity of reservoir hosts and their associated virus variants, making prevention, control and elimination

193 efforts increasingly complex (Fisher et al. 2018). The virus causes fatal encephalomyelitis once it reaches the brain, resulting in nearly 60,000 human deaths worldwide per year (World Health Organization 2018). Throughout its evolutionary history, RABV becomes compartmentalized by both geographical area and animal species leading to distinct variants that establish sustained transmission networks (Fisher et al. 2018, Troupin et al. 2016). Emerging rabies epizootics occur when ecological opportunities and viral establishment in a new host overcome geographical and species barriers (Fisher et al. 2018). During epizootics, multiple RABV variants with different degrees of genetic divergence may circulate simultaneously in the same host species, namely dogs, or in different host species such as dogs, skunks, raccoons, mongoose, as well as multiple species of bats. Furthermore, these complex assemblages of RABV variants and their specific reservoir hosts may establish independent rabies transmission cycles in either circumscribed or overlapped geographic regions (Lembo et al. 2007).

Phylogenetic analysis coupled with mapping of variants can help simplify and depict such complexity.

The RABVs have been grouped into multiple different variants, clades or lineages as more detailed phylogenetic information becomes available (Troupin et al. 2016,

Velasco-Villa et al. 2017). Currently, known RABVs are divided into two major clades, bat-related and dog-related (Troupin et al. 2016, Velasco-Villa et al. 2017). The Africa

1a, 1b and Africa 2 subclades (minor clades) of the dog-related clade are of special interest to this study. Africa 1a circulates in northern and eastern Africa, while Africa 1b circulates in central and southern Africa. Africa 2 circulates in central and western Africa

(Nadin-Davis 2013). The geographical separation of the Africa 1 and 2 lineages has been

194 complicated by the more recent identification of the Africa 1 viruses (a and b) in west

African countries (Nigeria, Gabon, Ghana), where Africa 2 was thought to be the only lineage in circulation (Nadin-Davis 2013, Hayman et al. 2011). Identification of all clades, lineages and variants circulating in a region is essential for establishing surveillance and control efforts, as well as to follow up on progress toward elimination

(Mollentze et al. 2014a).

Modern molecular methods identify circulating variants through detection and analysis of subtle differences within the RABV coding genes. Such data provide insight into virus-reservoir relationships, virus diversity within a single reservoir host, patterns of transmission and dissemination, variant extinction events and breadth of viral evolution

(Mollentze et al. 2014a). For example, partial genome analysis identified independent

RABV cycles being maintained in certain parts of South Africa by bat-eared fox

(Otocyon megalotis) and black-backed jackal (Canis mesomelas) populations; and in

Mexico not only revealed independent cycles in wildlife, but also independent geographically circumscribed disease pockets or foci associated with the dissemination of a long-standing dog rabies epizootic (Sabeta et al. 2007, Zulu et al. 2009, Velasco-Villa et al. 2005). In order to identify such sylvatic transmission cycles, comprehensive metadata of all existing gene sequences within and surrounding a target area must be available.

The N-gene is the most commonly sequenced RABV gene worldwide (13,002 sequences in GenBank) (Clark et al. 2016), providing a robust comparison with RABVs circulating within a target area. Other genes, although equally informative, have a poor grasp of such global diversity. The N-gene is widely used because it is the second most

195 conserved of the lyssavirus viral proteins while still bearing a relatively high degree of genetic diversity within short segments of the gene among variants associated with different reservoir host and geographic lineages (Troupin et al. 2016, Velasco-Villa et al.

2005, Clark et al. 2016). The use of this approach, together with an efficient surveillance system for disease detection in animal populations, has allowed for detailed descriptions of the distributions of major rabies spatio-temporal foci (disease pockets) as well as likely hosts responsible for maintenance (Mollentze et al. 2014b).

Throughout Sub-Saharan Africa, knowledge of RABV dynamics is greatly lacking. This is largely due to challenges in budget, surveillance, diagnostics and reporting (Wunner and Conzelmann 2013). Thereby, few sequences are publicly available for this region (Nel 2013). Ethiopia is a country for which such information does not exist but the need is becoming increasingly essential due to increasing control efforts. This country has long been among the most severely RABV-affected countries on the African continent, with a national annual incidence rate of 1.6/100,000 population human RABV deaths (Brunker et al. 2015, Deressa et al. 2013). A zoonotic disease prioritization workshop held as Ethiopia’s first step in engagement with the U.S. CDC

Global Health Security Agenda identified RABV as the number one priority disease among 43 reviewed zoonotic diseases (Pieracci et al. 2016). Earlier studies in Ethiopia have identified the circulation of the Africa 1a clade (Deressa et al. 2015a). However, limited information exists regarding the large-scale genetic diversity of RABVs circulating in dogs or the existence of alternative rabies cycles maintained by other mammalian species.

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This study seeks to have an insight of the rabies epizootic affecting dogs in

Ethiopia during the last decade, to set up a baseline to monitor progress on control and elimination strategies. More specifically, the objectives include the following: 1) investigate the overall genetic diversity of RABVs associated with dogs in Ethiopia and determine if variants are geographically circumscribed (locally maintained in rabies foci or disease pockets), 2) determine the relationship between dog-maintained RABV variants in Ethiopia and those of other African countries and 3) characterize the RABV variants found in other mammal species and assess the role of dog-mediated RABV transmission in the dissemination of RABV across the country and the region. Identifying

RABV reservoirs and their transmission cycles within Ethiopia will allow for proper targeting of surveillance, control and elimination efforts.

Materials and Methods

Definitions

There is some variability surrounding the use of certain molecular terms therefore, it is important to first provide a definition of terms. In this paper, a RABV variant is defined as a virus that has been determined to be genetically distinct repeatedly over a period of time based on nucleotide sequence data. Genetically distinct means that the sequence will segregate into monophyletic clusters or clades (common ancestor and all of its descendants) located at the most external nodes within a phylogenetic tree. These external nodes are often associated with a specific host species and/or with distinctive geographically-circumscribed disease pockets associated with the same animal species

(Velasco-Villa et al. 2017, Nadin-Davis et al., 2017). A lineage is defined as a distinct monophyletic cluster located at more internal nodes which are often associated with the

197 same taxonomic group (e.g. multiple geographic variants of dog-maintained RABV) and have a well-defined geographic distribution (Velasco-Villa et al. 2017). A single lineage may therefore contain multiple variants. Dog-maintained viruses are defined as those that are transmitted exclusively among dogs (Velasco-Villa et al. 2017). Dog-derived viruses are viruses that were previously dog-maintained lineages but, through repeated spillover events, were able to become established within a new population of wild terrestrial meso-carnivores in which transmission cycles became independently perpetuated (Velasco-Villa et al. 2017). Lastly, a pocket refers to distinct virus variants compartmentalized by species and/or geographical area that have established sustained transmission networks (Lembo et al. 2007, Velasco-Villa et al. 2017).

Samples

We obtained sequences from 229 samples originating in both wild and domestic animals collected throughout different regions of Ethiopia (Amhara, Oromia, Somali,

Southern Nations Nationalities and People’s Region and Tigray) (Figure 11). The

Ethiopian Public Health Institute (EPHI) collected these samples during the period 2010-

2017 (Tables 5-9). We selected samples for sequencing from a larger group of samples

(366 DFA-tested brain tissue samples) that had been stored in a sample repository at

EPHI.

Selection criteria for sequencing included species, geographic distribution, time period and condition of the tissue. We sequenced partial N-genes from the selected samples (186/229) spanning the period 2012-2017 (Tables 5-7) after confirming them to be positive by a real-time RT-PCR LN34 assay (Wadwah et al. 2017). Only samples with cycle threshold values of 25 or below were sequenced. Complete N-gene sequences

198

(Markotter et al. 2006, Smith 1995) for 43/229 samples were obtained at CDC from previous collaborations with EPHI (Tables 8-9).

MEGA7 software version 7.0 (Kumar et al. 2016) was used to create maximum likelihood trees, with substitution rates estimated using the time reversible model G+I and applying bootstrap calculations with 500 iterations. Once identical sequences were removed from the analysis, we used ClustalX (Larkin et al. 2007) to carry out a comparison of Ethiopian sequences against reference sequences obtained from GenBank representing major extant RABV variants across Africa (Table 10). All sequences were edited using BioEdit (Hall 1999) to partial N-gene fragments of 726bp long, located between positions 600 and 1400, based on SAD-B19 reference sequence (Conzelmann et al. 1990). Using the 43 complete N-gene sequences from Ethiopia, we created a second maximum likelihood tree for comparison with RABVs circulating in Somalia and the

Sudan for which only 555 bp long stretches between positions 1 and 555, were available in GenBank (Clark et al. 2016, Hall 1999, Conzelmann et al. 1990, Johnson et al. 2004).

We then calculated nucleotide pairwise p-distances within and among all variants identified on the ML trees, using MEGA 7 software (Kumar et al. 2016).

199

Figure 11. Map Showing the Distribution of Received Samples from EPHI

Table 5. Number of Samples per Species Selected for Sequencing of N-Gene

Species Scientific Name Number Selected Domestic Dog Canis familiaries 164 Domestic Cat Felis catus 16 Side-Striped Jackal Canis adustus 4 Ethiopian Wolf Canis simensis 1 Fox/Unkown N/A 1 Domestic Cow Bos taurus 1

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Table 6. Number of Samples per Year Selected for Sequencing of N-Gene

Year Number Selected 2012 22 2013 20 2014 19 2015 51 2016 45 2017 29 No Date 1

Table 7. Number of Samples per Location/Region Selected for Sequencing of N-

Gene

Region Number Selected Oromia 85 Addis Ababa 63 Amhara 22 Southern Nations, Nationalities 13 and People’s Region (SNNPR) Somali 1 Unknown 3

Table 8. Number of Complete N-Gene Sequences from the Year 2010 by Species

Species Scientific Name Number Dog Canis familiaries 34 Cat Felis catus 3 Cow Bos taurus 1 Donkey Equus africanus 3 asinus Fox/Unknown N/A 1 Hyena Crocuta 1

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Table 9. Number of Complete N-Gene Sequences from the Year 2010 by

Location/Region

Region Number Addis Ababa 37 Oromia 2 SNNPR 1 Tigray 3

Table 10. GenBank Reference Sequences

Accession Species Country Year Clade

Number

AY500827.1 Ethiopian Ethiopia 2003 AF1a

Wolf

KP723638 Ethiopian Ethiopia 2014 AF 1a

Wolf

KX148199 Jackal Somolia 1993 AF1a KX148198 Dog Somolia 1993 AF1a KX148194 Dog Moracco 1989 Af1a KF155001 Cow Moracco 2009 AF1a KX148197 Dog Algeria 2015 AF1a KX148202 Dog Gabon 1995 AF1a KX148201 Dog Nigeria 1986 AF1a

KX148209 Dog Madagascar 2004 AF1c

KX148211 Dog Madagascar 1986 AF1c KT336436 Dog South Africa 2012 N/A KX148203 Dog Mozambique 1986 AF1a KX148103 Human South Africa 1981 AF1b KX148204 Dog Namibia 1992 AF1b KR906751 Dog Tanzania 2011 AF1b JX473840 Kudu Namibia 2009 AF1b

KR906790 Dog Tanzania 2012 AF1b

KR906739 Dog Tanzania 2004 AF1b KX148206 Dog Tanzania 1996 AF1b KX148207 Human Kenya 2014 AF1b KR906783 Dog Tanzania 2012 AF1b KR906745 Dog Tanzania 2010 AF1b

Continued

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Table 10 continued

Accession Species Country Year Clade

Number

LC029889 Cattle Uganda 2009 N/A

KX148205 Dog Rwanda 1994 AF1b KR906735 Dog Tanzania 2008 AF1b KX148208 Dog Central 1992 AF1b African Republic EU886636 Red Fox Austria 2006 N/A EF206719 SAG2 N/A

FJ913470 ERA-VC China 2007 N/A GQ918139 CVS-11 N/A JQ944709 EPHVAC Ethiopia 2008 N/A

GU565704 Flury-HEP 1948 N/A KX148101 Human Egypt 1979 N/A KF154998 Dog Israel 1950 N/A KX148233 Dog Cote d' 1992 N/A Ivoire KX148236 Dog Mauritania 1993 AF2 KX148235 Dog Cote d' 2001 AF2 Ivoire KX148234 Dog Burkina 1986 AF2 Faso KX148230 Dog Burkina 1995 AF2 Faso KX148241 Dog Chad 1996 AF2 KF977826 Human Central 2011 AF2 African Republic: Bangui KX148240 Dog Chad 1990 Af2 Continued

203

Table 10 continued

Accession Species Country Year Clade Number

KC196743 Dog Nigeria 2011 AF2 KX148243 Dog Cameroon 1987 AF2 KX148242 Dog Cameroon 1994 AF2 KX148244 Dog Guinea 1990 AF2 KX148220 Mongoose South Africa 2013 AF3 KX148222 Mongoose South Africa 2014 AF3 KX148219 Honey Badger Botswana 2009 AF3

KX148223 Cat South Africa 2000 AF3 DQ099525 PM1503 2005 N/A

KX148200 Dog Ethiopia 1988 AF1a FJ947032 N/A Ethiopia 2009 AF1a FJ947030 Dog Ethiopia 2009 AF1a FJ947033 N/A Ethiopia 2009 AF1a U22637 Hyena Ethiopia 1995 AF1a AY502129 Dog Sudan 2003 AF1a AY502130 Dog Sudan 2003 AF1a

AY502131 Dog Sudan 2003 AF1a AY103015 Jackal Ethiopia 2002 AF1a AY502132 Jackal Ethiopia 2003 AF1a GU062189 Ethiopian Ethiopia 2009 AF1a Wolf EU853581 Dog Ethiopia 1988 AF1a EU853580 Cow Ethiopia 1987 AF1a AY502125 Dog Sudan 2003 AF1a AY502126 Dog Sudan 2003 AF1a AY502127 Dog Sudan 2003 AF1a AY502128 Dog Sudan 2003 AF1a

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Results

Maximum likelihood trees identified a complex assemblage of slightly different variants, which were embedded within a major lineage representing a dog-maintained rabies epizootic across the period 1988-2017 (Figure 12). We detected 14 dog-maintained

RABV variants, most of them statistically consistent, which diverged 1.3% on average across the period analyzed and multiple regions of Ethiopia namely, Oromia, the

Southern Nations, Nationalities and People’s Region (SNNP), Amhara and Somali

(Figure 12, Table 11). The genetic variation of viruses pertaining to the same variant ranged from 0 to 0.6% (100 to 99.6% identity values) spanning periods from 1 to 28 years, indicating fluctuating and overlapping viral populations over time (Table 11).

Clusters K, N and O comprised variants circumscribed over specific regions (Amhara,

Tigray and SNNPR), circulating over two to three consecutive years (Figure 12). These variants likely represented independent rabies pockets or rabies foci. Variant N, comprised viruses only obtained from rabid donkeys collected over one year. However, this variant showed the highest level of intra-cluster divergence, suggesting these viruses likely have been circulating in the region for longer than a year (Table 11). This observation contrasted with viruses from variant M, which were circulating for at least a period of 28 years and only varied 0.5% (Table 11).

We also determined pairwise distances among all variants (Table12). Variant P, was found to be the most divergent with and average nucleotide difference of 3.1% when compared with against all other variants A to O (Table 12). Variant P appeared circumscribed to the SNNP region in addition to being associated with side striped jackals (Canis adustus) circulating in the region for at least two consecutive years 2014-

205

2015. Most variants within the Addis Ababa/Oromia region seem to overlap temporarily and geographically (Figure 12), which unveiled a highly complex assemblage of co- circulating viruses within a long-standing rabies epizootic.

Our phylogenetic analysis found no evidence of contemporary importation of dog-maintained RABV from other African countries, indicating Ethiopia has its own dog- maintained and wildlife-maintained RABV variants, both of which are part of the Africa

1a clade (Figures 12, 13). However, dog-maintained RABV variants between Ethiopia,

Somalia and Sudan showed a common origin that indicated a historic rabies dissemination pattern consistent with a single epicenter in the region (Figure 13).

Ethiopian sequences were also compared against major rabies vaccine strains used in most vaccine formulations worldwide, including the EPH vaccine strain 2008. We found no evidence of vaccine-like strains circulating in rabid dogs during the period analyzed in Ethiopia (Figure 12)

206

ETH104HYOROMIA10 76 ETH30DGADDIS2010 Cluster B ETH95DGADDIS2010 ETH1351DGADBOLE15 Cluster G 76 ETH1614DGADADDISKETEMA12 ETH9DGADDIS2010 63 Cluster E ETH1517DGADYEKA17 ETH1544DGADNLAFTO17 59 ETH1466DGAMHARAENSARO17 Cluster F ETH1627CTOROSULULTA12 ETH1387DGOROLIYU15 76 ETH1489DGOROWECHESHA17 Cluster A ETH1509DGADADDISKETEMA16 ETH1432DGADKIRKOS16 ETH1473DGOROBURAYUNOWFINFINNE1 53 Cluster H ETH134DGADDIS2010 Ethiopia dog-mediated ETH1610DGADADDISKETEMA12 ETH1433ADADDISKETEMA16 64 long-standing rabies ETH1314DGOROBRUYW15 Cluster I 82 ETH1527CTADLITA16 epizootic 1988-2017 ETH29DGADDIS2010 ETH1604DGOROSULULTA12 ETH1DGADDIS2010 62 ETH1629CTADARADA13 ETH1615DGADGULLELLE13 ETH1312DGOROLIYU15 92 55 Cluster J ETH26DGADDIS2010 ETH1598DGOROMIAADAMA13 ETH1388DGOROBISHOFTU15 95 ETH1479DGORODUKEM17 ETH1329DGORODURBA15 63 Cluster C ETH1640DGADBOLE12 ETH1612DGADKIRKOS12 86 Cluster L ETH28DGADDIS2010 ETH1571DGAMHARACHACHA13 Cluster K 96 ETH1290DGAMHARANSHEWA15 70 KX148200EthiopiaDgAF1a*1988 94 ETH1291DGSNNPRAMBA15 Cluster M 89 ETH1580DGORODUKEM12 ETH1452DGSNNPRAZENA16 92 Cluster O ETH1382DGSNNPRHOSAENA15 94 ETH116DYTIGRAY10 Cluster N 100 ETH122DYTIGRAY10 ETH1557FXSNNPRLERA15 ETH1555FXADADDISKETEMA15 Cluster P Rabies wild-life cycle in 100 ETH1556FXSNNPRSILTE14 side-striped jackals Africa 1a, Somalia, 1993 100 Africa 1a, Algeria, Morocco, 1989-2015 100 73 Africa 1a, Gabon, Nigeria, 1986-1995 96 Africa 1c, Madagascar, 1986-2004 75 99 Africa 1b, Sub-Saharan Africa, 1991-2014 99 ERA-derived Vaccine strains SAG2 99 100 GU565704 Flury-HEP*1948 100 CVS-derived vaccine strains, EPH Vaccine, 2008 98 100 Africa 4, Egypt, Israel, 1950-1979 Africa 3, South Africa, Botswana, 2000-2014 100 Africa 2, West Africa, 1986-2011 100

0.020

Figure 12. Maximum Likelihood Tree of Partial N-gene Sequences and

Reference Sequences

207

Table 11. Intra-Variant Average Pairwise Genetic Distance (P-Distance)

from all Variants Identified in the ML Tree Depicted in Figure 12.

Variant Dissimilarity (%) Afr 2 3.414308373 ETH B 0 ETH G 0 ETH E 0.41322314 ETH F 0.091827365 ETH A 0 ETH H 0.275482094 ETH I 0.220385675 ETH J 0.459136823 ETH C 0.137741047 ETH L 0.137741047 ETH K 0 ETH M 0.459136823 ETH O 0.275482094 ETH N 0.550964187 ETH P 0.183654729 Afr1aSm 0.275482094 Afr1aMA 1.744719927 Afr1aGbN 2.066115702 Afr!cMad 0.275482094 Afr1b 2.883379247 VaccERA 0.091827365 VaccCVS 1.60697888 Afr 4 1.101928375 Afr 3 8.034894399

*Dissimilarity values expressed in percentage

208

Table 12. Averaging Intergroup Pairwise P-Distance

Oromia/Addis Ethiopian Ethiopian Ethiopian Ethiopian Somolia Africa Africa Africa ERA/SAD PV- Africa Africa Africa Ababa Outliers Wolves Donkeys Side- Dog 1a 1c 1b Vaccines Related 4 2 3 Striped and Vaccines Jackals Jackal CVS PM HEP Ethiopia Oromia/Addis 0 Ababa Ethiopian 0.016 Outliers Ethiopian 0.017 0.011 Wolves Ethiopian 0.022 0.016 0.016 Donkeys

Ethiopian 0.033 0.026 0.027 0.03 Side-Striped Jackals Somolia Dog 0.031 0.027 0.028 0.034 0.036 and Jackal

Africa 1a 0.047 0.043 0.044 0.048 0.05 0.045 Africa 1c 0.046 0.041 0.043 0.045 0.051 0.049 0.046 Africa 1b 0.065 0.058 0.059 0.064 0.063 0.064 0.055 0.06

ERA/SAD 0.061 0.055 0.057 0.057 0.064 0.07 0.063 0.05 0.073 Vaccines

PV-Related 0.08 0.073 0.076 0.08 0.078 0.084 0.075 0.072 0.082 0.07 Vaccines CVS PM HEP Ethiopia Africa 4 0.084 0.079 0.078 0.087 0.086 0.084 0.079 0.08 0.093 0.087 0.093

Africa 2 0.144 0.144 0.145 0.147 0.147 0.15 0.15 0.153 0.157 0.147 0.15 0.141 Africa 3 0.145 0.143 0.143 0.146 0.145 0.147 0.145 0.142 0.153 0.137 0.147 0.147 0.158 0

209

Africa 1a

og og

2018 -

epizootic epizootic

1987 Ethiopia d Ethiopia

0.02 0

Africa 1a Africa Morocco, Morocco,

1989 Algeria

Figure 13. Maximum Likelihood Tree Ethiopia Complete N-Gene Sequences Compared to Somalia and the Sudan

210

Discussion

Through the use of partial and complete N-gene sequencing in combination with passive surveillance efforts, we were able to demonstrate a complex assemblage of co- circulating dog-maintained RABV variants. This is consistent with a long-standing dog- rabies epizootic evolving throughout Ethiopia over at least the past 29 years. We were unable to identify a consistent geographical clustering pattern among most of these overlapping dog-maintained variants, which indicates widespread dissemination with no significant barriers across the landscape, also reaching some mammalian wildlife species.

Additionally, we found no dog-maintained RABV variants introduced from other African countries circulating throughout Ethiopia, however a common rabies dissemination epicenter in the region encompassing Somalia, Sudan and Ethiopia was identified

(Johnson et al. 2004). Of great significance is the finding that side-striped jackals (Canis adustus) from the SNNP region may be maintaining their own dog-derived RABV variant.

The low level of genetic variability (from 0 to 2%) among RABVs in domestic dogs across Ethiopia over an extended time period, supports the existence of a long- standing dog-maintained RABV epizootic (Table S8). Low detectability of phylogeographical disease pockets in other regions of the country as well as introductions of RABV variants from other African countries may be reduced as a result of a sampling bias toward the capital city Addis Ababa. However, sample sequences from other regions including Amhara, Tigray, Somali and SNNP segregated in region-unique clusters, which likely reflect compartmentalization of the most common RABV variants affecting these regions. This observation is consistent with geographical barriers that exist throughout

211 the country (e.g. montane highlands, rivers, rift valleys). The lack of more isolates pertaining to these putative geographic dog-maintained disease pockets is likely the reflection of limited rabies surveillance out of Oromia and Addis Ababa.

Anthropogenic influence has created a highly connected environment that has allowed RABV primarily associated with dogs to overcome geographical and species barriers. Ethiopia is the second most populated country in Africa with a human population estimated to be nearly 105 million people (United Nations 2017). Pastoralism, a lifestyle characterized by long-distance travel on foot often accompanied by dogs in order to protect livestock, is a way of life for nearly 10 million Ethiopians (SOS-Sahel

Ethiopia 2008). Additionally, Ethiopia ranks 44th in the world for the longest road network, including 110,414 km of roads and 659 km of railway (United States Central

Intelligence Agency 2018).

The history of RABV control throughout the country may contribute to such complex RABV variant assemblages among dog populations (Fekadu 1982). Both

Colombia and the southern portion of Mexico are of comparable size with similar histories of canine rabies to Ethiopia (Pieracci et al. 2016, United States Central

Intelligence Agency 2018). Both countries showed similar variant assemblages with some geographical structuring (Velasco-Villa et al. 2005, Páez et al. 2007). This is likely a result of similarities in how these countries have addressed rabies control over time.

Mexico and Colombia initiated successful national vaccination in their dog populations programs in the early 1990’s (Páez et al. 2007, Lucas et al. 2008, Cediel et al. 2010), creating intangible vaccination barriers that were able to contain spreading. Likewise,

Ethiopia has recently initiated dog vaccination campaigns and other control efforts that

212 along with topographical barriers may have contributed to some of the geographical structuring in the circulating RABVs. During the 2001-2009 period, production of Fermi- type vaccine for animal use was roughly 90,000 doses, which is below the number required to reach 70% vaccination coverage in the overall dog population (Deressa et al.

2015b, Deressa et al. 2010). Public health authorities believe that the continued high occurrence of rabies throughout Ethiopia can largely be attributed to the poor management of free-roaming owned dogs, the large population of stray dogs without regular vaccination, and little epidemiological data on dog demography throughout the country (Deressa et al. 2015b, Reta et al. 2014).

The putative epicenter of rabies dispersal in Addis Ababa, could be attributed to the sample collection bias. However, the dog population in Addis Ababa has been estimated to be between 250,000 and 350,000 dogs (Deressa et al. 2010). It has also been noted that the dog population is higher in urban areas where there are more people than in rural areas (Reta et al. 2014, Zewde 1998, Tschopp et al. 2016). The mobility of these dogs, and dogs throughout the country, is significantly enhanced by human-mediated movement (Tschopp et al. 2016). In Tanzania, canine RABV dispersal showed evidence of long-distance spreading out of locally endemic areas as a result of human- mediated movement, in some instances associated with translocation coming from rural areas where pastoralists were likely to migrate to (Wunner and Conzelmann 2013,

Morters et al. 2014). Further, in North Africa, road distances proved to be a better predictor of the movement of canine RABV than other factors such as accessibility or raw geographical distance (Talbi et al. 2009).

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We did observe phylogeographic clustering between countries in Africa, indicating no apparent dissemination of RABV from neighboring countries (Johnson et al. 2004). Previous studies have found that RABV variants from the Sudan and Ethiopia form a monophyletic cluster, consistent with our findings indicating a historical dissemination epicenter in the region (Johnson et al. 2004). The majority of rabies dispersal is occurring on a within-country scale, which was found to be the case in

Tanzania as well (Wunner and Conzelmann 2013). As was suggested for Tanzania

(Wunner and Conzelmann 2013), control strategies in Ethiopia should first be targeted at a national level where the majority of transmission is occurring. Nonetheless, enhance rabies surveillance along the country’s borders, by means of decentralized rabies diagnosis, may help to capture sporadic events of rabies introduction from neighboring countries.

The discovery of a RABV cycle being independently maintained in the side- striped jackal (Canis adustus) population within the SNNP region is of great significance.

The genetic divergence found between the SNNP jackal RABV variant and the Addis

Ababa/Oromia dog epizootic RABV variant is consistent with the extent of genetic variation observed in established RABV variants of dog origin reported in gray foxes and skunks in the U.S., crab-eating foxes in Brazil, and ferret-badger in China and Taiwan

(Velasco-Villa et al. 2008, Lin et al. 2016, Liu et al. 2010, Carnieli et al. 2013). It has been extensively documented that long-standing dog-maintained rabies epizootics favor the establishment of dog-derived RABV variants in terrestrial carnivore populations, thus augmenting rabies exposure sources for humans and domestic animals in the long term

(Hayman et al. 2011 et al. 2014, Velasco-Villa et al. 2008, Lin et al. 2016, Liu et al.

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2010, Carnieli et al. 2013, Badrane and Tordo 2001, Bourhy et al. 2008). As major urbanization continues to increase, interactions between humans and their associated fauna (e.g. dogs) with naive wildlife populations will increase, creating opportunities for the establishment and expansion of new RABV variants (Velasco-Villa et al. 2017, Talbi et al. 2009, Velasco-Villa et al. 2008).

Side-striped jackals maintain rabies transmission independent of domestic dog populations in commercial farming sectors of Zimbabwe, where ecological conditions are favorable for supporting high jackal densities (Bingham and Purchase 2002). One reason that the newly identified Ethiopian RABV variant found in this species might be confined to the SNNP region could be due to the fact that it is the most rural of all of the regions in

Ethiopia, where 93.2% of the population in the region is devoted to farming (Government of Ethiopia 2017). Though the majority of farms in this region are small holder farms rather than commercial farms (Tefera and Tefera 2013), it seems to be the rural dwellings and farm buildings rather than the size of the farms that the jackals prefer (Hoffmann

2014). Additionally, this region is largely bordered by the southwestern highlands, the

Bale mountains and the rift valley, all of which could contribute to geographical isolation

(United States Central Intelligence Agency 2018, Government of Ethiopia 2017, Tefera and Tefera 2013).

This finding indicates that elimination of the virus in dogs alone will not be effective to eliminate this jackal RABV variant as long as the ecological conditions allowing side-striped jackal populations to maintain independent transmission are present. If RABV is not eliminated in all maintenance populations, an endless cycle of re- introductions from wildlife to dogs or from dogs to wildlife will continue (Velasco-Villa

215 et al. 2005, Velasco-Villa et al. 2008). Rabies surveillance in wildlife and dogs must be strengthened, particularly in this region, to determine if this wildlife cycle continues.

Conclusions

This investigation describes the dynamics of major rabies reservoir hosts and their associated viral variants throughout Ethiopia over a 29-year period. Identification of what variants are circulating, where these variants are circulating and which species transmit them is critical for control efforts. The existence of a long-standing dog rabies epizootic in Addis Ababa/Oromia highlights the need for permanent vaccination strategies that will reduce the diversity of RABV variants in dogs, as well as limit translocation of dogs throughout the country. Additionally, surveillance in dog and wildlife populations, particular side-striped jackals, needs to be initiated and maintained long-term.

Use of partial N-gene sequences proved to be an effective method for identifying large-scale RABV transmission dynamics throughout Ethiopia. Because the larger scale trends have now been identified, studies using whole genome sequencing should be carried out in order to increase resolution for the identification of local disease pockets on smaller geographic scales. The decentralization of RABV surveillance and diagnostic efforts will be necessary in this regard to provide local-level information throughout the country and rule out the existence of local dog-rabies pockets. Results from this study provide the necessary baseline data required to monitor progress and effectiveness of more systematic long-term intervention strategies.

Containing the spread of rabies virus in dogs will require: more effective dog vaccination campaigns throughout the country, implementation of dog population management strategies, and legislation that impacts trans-boundary animal travel. More

216 notorious fragmentation and local extinction of virus variants will occur, producing a more distinct geographical structure in which disease pockets across different regions and within regions with natural topographical barriers will begin to be more consistently observed (Lembo et al. 2007, Mollentze et al. 2014b, Velasco-Villa et al. 2008). If RABV control measures are effective, these disease pockets will begin to disappear (Mollentze et al. 2014b, Velasco-Villa et al. 2008).

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Chapter 4: Identification of Contact Rates in Terrestrial Carnivores at Communal

Foraging Site in Ethiopia

Abstract

Controlling zoonotic pathogens requires detailed knowledge of transmission dynamics within the animal reservoir. Such knowledge requires quantification of species abundance, characterization of species geographic distribution, and quantification of contact rates among affected species. This information has not yet been gathered in

Ethiopia, a country with many opportunities for transmission of zoonotic pathogens.

Our research seeks to characterize reservoirs of zoonotic pathogens at sites in

Ethiopia considered high-risk for pathogen transmission. Specifically, we investigated species composition, abundance, and contact rates within- and between-species at five communal scavenging sites located throughout Ethiopia.

We placed camera traps at four slaughter plants and one waste disposal facility in four cities with different landscape characteristics throughout Ethiopia. We used this data to determine which wildlife visited these communal scavenging sites and to quantify maximum group size, average group size at each location, species-specific temporal activity patterns, within-species contact rates, and between-species contact rates.

We found that species abundance differed across the five study sites. Domestic dogs (Canis lupis familiaris), cats (Felis catus), spotted hyenas (Crocuta Crocuta) and

218 mongoose (Herpestidae, undefinable) were most abundant at the majority of study sites while black-backed jackals (Canis mesomelas), African golden wolves (Canis anthus) and honey badgers (Mellivora capensis) were less abundant. We also found temporal patterns in abundance with domestic dog and cat sightings occurring during the day while all other species sightings occurred throughout the night. Within-species contact rates were highest in the domestic dog population followed by the spotted hyena and domestic cat population, respectively. Overall, within-species contact rates were higher than between-species contact rates. Highest between-species contact rates occurred between spotted hyenas and domestic cats.

Species abundance was consistent with the abundance-distribution relationship in that each site contained a few abundant species and many rare species. However, the identity of the most abundant species differed among sites. Within-group contact rates coincided with known species-specific social behavior while between-group contact rates coincided with temporal activity patterns. These data can be used to help model pathogen transmission and inform intervention strategies for prevention and control of zoonotic pathogens by providing a measure of contact as well as information about which types of interactions to target.

Introduction

Zoonotic diseases continue to pose a major threat to public health around the world. Today, 60.3% of emerging infectious diseases are zoonotic and the majority originate in wildlife (71.8%) (Jones et al. 2008, Taylor, Latham & Woolhouse 2001).

Yet, identifying which wildlife species drive zoonotic disease transmission is difficult.

Most wildlife diseases are maintained in a reservoir comprised of one or more

219 epidemiologically connected populations that transmit the pathogen to other populations

(Haydon et al. 2002). Wildlife that are the best candidate hosts for maintenance of zoonotic pathogens are those species that have adapted to ecosystem changes induced by the introduction of large-scale commercial agriculture and urbanization (Bingham 2005).

Living in close proximity with humans and their domestic animals, these wildlife species provide opportunities for transmission of endemic and newly emerging infectious diseases between livestock, wildlife, and humans (FAO-OIE-WHO 2010). Understanding how wildlife contribute to within- and between-species transmission can help predict the ability of a pathogen to persist within a wildlife population and the chance that the pathogen will cause outbreaks in other populations (Fenton & Pedersen 2005, Viana et al.

2014).

To identify animal species that comprise the reservoir, we must determine which species are epidemiologically connected by contact. However, contact rates both within and between natural populations are difficult to measure. Radiotelemetry, global positioning systems (GPS) and passive integrated transponder (PIT) tags have previously been used to quantify contact rates in wild animal populations (Real & Biek 2007,

Sutherland et al. 2005, Ryder et al. 2012, Hirsch et al. 2013). However, these methods are costly, time intensive, and often underestimate the number of contacts unless all individuals are tagged or the proportion of the population that is tagged is known (Real &

Biek 2007). Moreover, these methods can also be especially hard to use with large carnivores that are difficult to trap and can easily remove such devices.

A new approach to identify contact rates among wildlife is to use camera traps.

Camera traps are low-labor, low-cost, non-invasive, robust to variation in ground

220 conditions and climate, can be used to gain information from highly cryptic species and are equally efficient at collecting data by day and night (Rowcliffe et al. 2008, Trolliet et al. 2014). While still considered a form of indirect observation, this method allows for observation of animal behavior. These cameras take either a still image or a video of an individual or a group of individuals that enter the location-specific zone of detection

(Caravaggi et al. 2017). Such images can then be linked with other data such as date, time and temperature. Cameras placed at communal scavenging sites can provide data on interactions and record contacts both within and between species. These data can then be used to calculate daily or nightly contact rates (Kelly et al. 2012).

Here, we use camera traps to identify contact rates among wildlife populations in

Ethiopia. Ethiopia has been designated as a country with high relative risks for outbreaks of zoonotic pathogens originating in wildlife (Jones et al. 2008). Current priority zoonotic diseases in Ethiopia include rabies, anthrax, brucellosis, leptospirosis and echinococcosis

(Piericci et al. 2016). Though on the decline due to control efforts, zoonotic diseases including tuberculosis and malaria cause a significant burden of disease as well

(Misganaw et al. 2017).

Ethiopia is part of the eastern Afromontane biodiversity hotspot made up of both highland and lowland habitats (Critical Ecosystem Partnership Fund 2011). Throughout the country, there are many opportunities for contact within and between species due to competition for food and water, high density and mobility of animal populations

(especially domestic dog populations) and crop or livestock raiding by wildlife (Mojo,

Rothschuh & Alebachew 2014). Specifically, communal scavenging sites such as water points, grazing areas, slaughterhouses, and waste disposal facilities attract multiple

221 species, as well as multiple individuals of the same species, to the same location. These sites are areas of high-risk for pathogen transmission both within and between species.

However, contact rates within and between animal populations at such high-risk areas in

Ethiopia have never been quantified.

In the current study, we use camera trap data collected from communal scavenging sites throughout Ethiopia to achieve three major objectives. The first objective is to determine which wildlife species are present at sites throughout Ethiopia.

The second objective is to identify patterns of species abundance at sites throughout

Ethiopia. The third objective is to quantify and characterize within and between species contact rates throughout Ethiopia. These data can be used to help mitigate risk through intervention strategies using a One Health approach that will target the human-animal- ecosystem interface via key sites and species.

Methods

Definitions

Contact/Interaction- Number of potential pairwise interactions between individuals within the same camera frame. Because images were taken at a communal feeding site, the potential for direct contact is highly likely though not necessarily caught on camera. Additionally, individuals are feeding off of the same food sources.

Intraspecies contact/interaction- Here referred to as within-species contact. Number of potential pairwise interactions between individuals of the same species within the same camera frame/image.

Interspecies contact/interaction- Here referred to as between-species contact. Number of potential pairwise interactions between individuals of different species pairs within the same camera frame/image.

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Reservoir- One or more epidemiologically connected populations in which a given pathogen can be permanently maintained and from which infection is transmitted to the defined target population or population of interest (Haydon et al. 2002).

Study Sites for Camera Trap Surveys

We conducted camera trap surveys in four different cities in Ethiopia: Addis

Ababa and Goba of the Oromia region, Awash of the Afar region, and Awassa of the

Southern Nations Nationalities and Peoples’ region. These cities were selected to represent highland, lowland, urban and rural locations (Figure 14).

We placed camera traps at slaughter plants and waste disposal facilities. These locations contain resources cashes that are easily accessible to numerous wildlife species and act as communal scavenging sites. Waste material consisted primarily of meat scraps and bones. Additionally, the consistency in size and management practices permit comparisons across sites. We placed one camera trap at the central slaughter plant in each city, which was identified by the municipality. Slaughter plants had an average area of

4,058.7m2 (Table 14). In Awassa, we placed one additional camera trap at the central waste disposal facility. The waste disposal facility had an area of 13,767m2 (Table 14).

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Awash National Park Addis Ababa

Bale Mountains Awassa

Figure 14. Camera Trap Survey Locations

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Table 13. Area (in m2) and Total Recording Time (in Minutes) for all Study Sites.

Study Site Area m2 Total recording time (min)

Addis Ababa Slaughter 3154.61 13070 Plant

Bale Slaughter Plant 4502.49 3689

Awash Slaughter Plant 4241.6 4633

Awassa Slaughter Plant 4336.31 2970

Awassa Waste Disposal 13767 1544 Facility

Total 30,002.01 30,002.01

Camera Trap Selection and Setup

We utilized five Reconyx Hyperfire model PC900 camera traps specialized for covert wildlife research (Reconyx, Holmen, WI). These models capture high quality images (1080HD day/monochrome at night), record at night (no glow infrared night vision), store large amounts of information (32GB), resist harsh weather conditions, and perform at a range of temperatures (between -40°C and 60°C).

We attached cameras at locations that were slightly above eye level for most canid species. When possible, we attached cameras to trees at 1-2m above the ground.

When trees were not available, we attached cameras to pole mounts at roughly 0.75 m

225 above the ground. We mounted all cameras at a slightly downward angle of roughly 5° below horizontal as recommended by the manufacturer, which produced a field of view of 35°.

We used the time lapse setting and took one photograph per minute over 17-hour periods starting at 1800 (6:00pm) and ending at 1100 (11:00am), which allowed us to observe peak activity for nocturnal and diurnal species. We could not always capture the entire 17-hour period due to human interference (e.g. moving or stealing of the camera) and report recording time in Appendix A (Table 22). We recorded at each site for an average of five (range of three-thirteen) 17-hour periods for a total of 25,906 min (431 hrs) (Table 13) and 25,906 photographs. We checked cameras every other day and restored cameras to their original position if they had been moved.

Camera Trap Data Transcription and Analysis

We inspected each photograph. For each photograph, we manually recorded the study site, time, date, number of individuals present, and abundance of each species present. This information allowed us to identify which species were active at these sites and quantify maximum, minimum, and average group size at each site. We used maximum group size (Appendix A Tables 23-27) to create species rank-abundance curves (Figure 15). We also created rank-abundance curves for average group size for species observed at each site over all recording periods (Appendix A Table 28, Figure

19). We also identified the maximum number of individuals present each hour by species for all sites and then averaged this metric across all sites to visualize species-specific temporal activity patterns (Figure 16).

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We defined within-species contacts as the number of potential pairwise interactions between individuals of the same species. We quantified within-species contact rates in a three-step process. First, we counted the number of individuals of the same species in each photograph. Second, we considered all possible pairwise formations that could occur using the mathematical operation combination. For example, if we counted five individuals of the same species in a photograph in step one, then we would calculate how many ways we could select two out of the five individuals, or 5퐶2. In this example, we would calculate 10 pairwise possibilities. Third, we selected maximum within-species contact values from each of the 17-hour recording periods at each site to ensure that each contact event was an independent event of unique individuals interacting as opposed to a repeated measure. We then averaged these maximum values over all 17- hour recording periods for a given site in order to get within-species contacts per 17-hour recording period. Henceforth, we refer to these values as contact per period.

We defined between-species contacts as the number of potential pairwise interactions between individuals of different species. We quantified between-species contact rates in a three-step process. First, we counted the number of individuals of all species present in each photograph. Second, we considered all possible pairwise formations including animals of different species that could occur using the mathematical operation combination. For example, if we counted three individuals of the one species and two individuals of a second species in step one, then we would calculate how many ways we could select two out of the five individuals in a mixed species pair. We calculated this as 3퐶1푥 2퐶1, which simplifies to 3푥2. So, in practice, we multiplied the number of individuals of the first species by the number of individuals of the second

227 species. In this example, we would calculate 6 pairwise possibilities. If multiple interactions between the same two species occurred within a given 17-hour recording period, the maximum between-species contact number was used to ensure that each contact event was an independent event containing unique individuals as opposed to a repeated measure. We repeated this process for all pairs of species observed in the photograph. Third, we averaged results from step 2 over the four 17-hour recording periods conducted at the same site to account for variability between recording periods.

Henceforth, we refer to these values as between-species contacts per period.

Results

Across all study sites, we observed seven different species: domestic dog (Canis lupis familiaris), domestic cat (Felis catus), spotted hyena (Crocuta Crocuta), mongoose

(Herpestidae, genus and species undefinable), black-backed jackal (Canis mesomelas),

African golden wolf (Canis anthus), and honey badger (Mellivora capensis). We observed domestic dogs and cats at all sites. We observed spotted hyenas at all but the

Goba slaughter plant site. We observed mongoose in all cities except Awassa. We observed jackals only at the Addis Ababa and Awash slaughter plant sites. We observed honey badgers only at the Awash slaughter plant site (Figure 15). These observations indicate that the seven different species we observed have different geographic distributions.

Both the number of individuals observed and species composition varied by study site. We observed the largest number of individuals in a single image at the Goba slaughter plant site. We observed the smallest number of individuals in a single image at the Awash slaughter plant site. The average number of individuals observed at a site was

228 consistent with maximum number of individuals observed, except for the Awassa waste disposal facility and Awash slaughter plant site. The Awassa waste disposal facility had the second lowest maximum number of individuals present, but the lowest average number of individuals present (Appendix A Tables 28-29 But, the Awash slaughter plant had only one additional individual present on average than the Awassa waste disposal facility. We observed the greatest variation in species composition at the Awash slaughter plant site followed closely by the Addis Ababa slaughter plant site. Spotted hyenas were most abundant species at the Addis Ababa slaughter plant, Awash slaughter plant, and

Awassa waste processing facility, while domestic dogs were most abundant at the Goba slaughter plant site, and domestic cats were most abundant at the Awash slaughter plant site. These observations indicate multiple individuals from different species frequent these communal scavenging sites, but the species abundance and composition differs by site.

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Figure 15. Species Rank-Abundance Curve for all Species Across all Sites Using

Maximum Group Size Within a Species Present at Each Site. Spotted hyena is

represented by a triangle (▲), domestic dog by a square (■), domestic cat by a

circle (●), mongoose by a diamond (♦), blacked-backed jackal by a hollow

upside-down triangle (˅), honey badger by a star (*) and African golden wolf by a

cross (X).

The number of animals observed varied throughout the recording period.

Domestic dogs and cats were the only species active during the daytime. Dogs showed peak activity between 5:00 am and 11:00 am, but were observed during the night as well

(Figure 16). Cats showed peak activity between 3:00 am and 5:00 am and again at 8:00 pm. Cats were also observed regularly throughout the night. We observed all wildlife species including spotted hyena, mongoose, black-backed jackal, African golden wolf and honey badger between the hours of 8:00 pm and 5:00 am, indicating that the wildlife displayed nocturnal behavior. Our observations indicate that different species are active

230 at different times of the day or night. Species activity at each site can be found in

Appendix A Figures 19-23.

231

10.00

9.00

8.00

7.00

6.00

5.00

4.00

3.00 Average Average Countof Individuals

2.00

1.00

0.00 12:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 6:00 7:00 8:00 9:00 10:00 11:00 AM AM AM AM AM AM AM AM AM AM AM PM PM PM PM PM PM Hours Dog Cat Spotted Hyena Mongoose Black-backed Jackal African Golden Wolf Honey Badger

Figure 16. Average Counts of Maximum Individuals Active per Hour at all Sites Combined. These data span the period

from 12:00am-11:00am and 6:00pm-11:00pm. Each species is represented by a different color as designated in the legend.

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From these data, we calculated within-species contacts per period. We found the highest average within-species contacts per period in domestic dogs at the Goba slaughter plant, followed by spotted hyenas at the Awassa slaughter plant, Addis Ababa slaughter plant, and Awassa waste disposal facility. We also calculated high within-species contacts per period for domestic dogs at the Addis Ababa slaughter plant and for domestic cats at the Awassa slaughter plant (Table 14). Overall, we calculated the highest within-species contacts per period at the Goba slaughter plant followed by the Awassa slaughter plant and the lowest contacts per period at the Awash slaughter plant site (Table

14). Our calculations indicate that within-species contacts per period varied by species and by site. Maximum within- species contact rates for each individual site can be found in Appendix A Tables 30-34.

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Table 14. Within-Species Average of Maximum Contacts per Period by Species and Site.

Site Dog Cat Spotted Mongoose Black- African Honey Total Hyena backed Golden Badger jackal Wolf Addis Ababa 5.77 0.23 11.77 0.08 0 0.23 0 18.08 Slaughter (0, 15, (0, 3, (0, 55, (0, 1, (0, 3, Plant 4.92) 0.83) 16.39) 0.28) 0.83) Goba 216 0 0 0 0 0 0 216 Slaughter (28, 528, Plant 230.92) Awash 1.40 2.80 0.40 0 0 0 0.20 4.80 Slaughter (0, 3, (1, 6, (0, 1, (0, 1, Plant 1.52) 2.05) 0.55) 0.45) Awassa 3.33 4.00 20.33 0 0 0 0 27.66 Slaughter (1, 6, (3, 6, (6, 45, Plant 2.52) 1.73) 21.46) Awassa Waste 0.33 0 11.00 0 0 0 0 11.33 Disposal (0, 1, (6, 21, Facility 0.58) 8.66) Total 226.83 7.03 43.5 0.08 0 0.23 0.2

The minimum, maximum and standard deviation for contacts per period are listed in parenthesis, respectively, below each measure of contacts per period.

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We also quantified between-species contacts per period. We calculated the highest between-species contacts per period between spotted hyenas and domestic cats at the Awassa slaughter plant and Awassa waste disposal facility followed by domestic dogs and cats at the Awash slaughter plant (Table 15). We calculated very low between- species contacts per period for all other species and sites. The lowest between-species contacts per period were calculated for the Goba slaughter plant site. Maximum between- species contacts for each site can be found in Appendix A Tables 35-39.

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Table 15. Between-Species Average of Maximum Contacts per Period by Species and Site.

Site Dog- Cat- Cat- Cat- Spotted Spotted Spotted Mongoose- Total Cat Spotted Mongoose Black- Hyena- Hyena- Hyena- African Hyena backed Mongoose Black- African Golden jackal backed Golden Wolf jackal Wolf Addis 0 0.31 0 0 0.69 0 0.08 0.08 1.16 Ababa (0, 4, (0, 4, (0, 1, (0, 1, Slaughter 1.11) 1.25) 0.28) 0.28) Plant Goba 0 0 0.25 0 0 0 0 0 0.25 Slaughter (0, 1, Plant 0.50) Awash 2.00 1.00 0.20 0.20 0 0.60 0 0 4.00 Slaughter (0, 4, (1, 1, (0, 1, (0, 1, (0, 1, Plant 1.58) 0) 0.45) 0.45) 0.55) Awassa 0 16.00 0 0 0 0 0 0 16.00 Slaughter (12, 21, Plant 4.58) Awassa 0 2.00 0 0 0 0 0 0 2.00 Waste (0, 5, Disposal 2.65) Facility Total 2.23 19.31 0.45 0.2 0.69 0.68 0.08 0.08 The minimum, maximum and standard deviation for contacts per period are listed in parenthesis, respectively, below each measure of contacts per period.

236

Discussion

Our study provides information about species composition, abundance, and contacts among terrestrial carnivores at communal scavenging sites throughout Ethiopia.

We observed domestic dogs, cats, spotted hyenas and mongoose at most sites but observed black-backed jackals, African golden wolves and honey badgers at only one site. We observed the greatest species richness at the Awash slaughter plant site and the lowest species richness at the Goba slaughter plant site. Domestic dog and cat sightings occurred during the day while all other species (domestic cat, spotted hyena, black- backed jackal, African golden wolf, honey badger and mongoose) sightings occurred during the night. Within-species contacts per period were highest in the domestic dog population followed by the spotted hyena and domestic cat population. Overall, within- species contacts per period were higher than between-species contacts per period. The highest between-species contacts per period occurred between spotted hyenas and domestic cats at the Awassa slaughter plant site. These results indicate that wildlife interactions at communal scavenging sites are complex and exhibit a substantial degree of spatial and temporal variation.

We observed an inverse relationship between species diversity and total numbers of individuals of any species sighted. The site with the highest species richness, the

Awash slaughter plant, was also the site with the lowest maximum number of individuals present. Conversely, the site with the lowest species richness, the Goba slaughter plant, was also the site where the greatest numbers of individuals were observed. Even though there was a more even distribution of species at the Awash slaughter plant site, the numbers of individuals within each species were low (Figure 15). Such patterns could be

237 the result of a variety of differences in resource availability or competitive exclusion. In

Zimbabwe, side-striped jackal (Canis adustus) and black-backed jackal (Canis mesomelas) populations were found to avoid one another despite having overlapping home ranges, with black-backed jackals remaining in grassland habitat while side-striped jackals favored areas of high human activity (Loveridge & Macdonald 2003). If this were true in Ethiopia, we might only see side-striped jackals at the slaughter plant sites even though black-backed jackals live in the same area. Therefore, there may be more competition between species at sites with fewer species present.

Previous community ecology studies have identified a distribution-abundance relationship, which predicts that communities will be comprised of a few abundant generalist species and many rare specialist species (McGill et al. 2007, Verberk 2011).

Our current study observed animal abundance data that are consistent with this relationship, but which species was most abundant differed by site. For example, dogs were extremely abundant at the Goba site, while cats and wildlife species were rare or unobserved. At another site, like the Addis Ababa slaughter plant, we still observed a few abundant species and many rare species, but different species ranked highest in abundance. In Addis Ababa, hyenas were most abundant, dogs were approximately half as abundant as hyenas, and the other five species were rare or completely unobserved. At all sites, mongoose, black-backed jackal, African golden wolf, and honey badger were rarely observed and either domestic dogs, domestic cats, or spotted hyenas ranked highest in abundance. Spotted hyenas were the most abundant species at urban sites while dogs and cats were most abundant at rural sites. Perhaps spotted hyenas’ strong dependency on human activity throughout Ethiopia (Yirga et al. 2017) allows them to adapt and thrive at

238 urban sites. Or, because spotted hyenas were most abundant during the nighttime while domestic dogs were most abundant during the daytime, perhaps species temporally partition the niche. Similar results showing temporal niche partitioning between wild dogs and spotted hyenas were observed in other sub-Saharan African countries (Hayward

& Slotow 2009).

These temporal patterns in abundance provide context for between-species contacts per period. Highest between-species contacts per period were calculated between spotted hyenas and domestic cats, the two most abundant species during the nighttime hours. Second highest between-species contacts per period were calculated between domestic dogs and domestic cats, the two most abundant species during the daytime hours. Domestic cats were observed during both daytime and nighttime hours, linking diurnal and nocturnal species. However, other between-species interactions that we did not observe in this study have been documented in other parts of Sub-Saharan Africa. For example, spotted hyenas kill and eat dogs (Craft et al. 2016). Pathogen transmission studies suggest contact between domestic dogs and wild carnivore populations and between different wild carnivore populations (Prager et al. 2012, Lembo et al. 2008, Craft et al. 2009). These interactions may be less likely at slaughter plants because food is so abundant. Further investigation into areas other than communal scavenging sights may help identify and quantify such contacts in Ethiopia.

Within-species contacts per period were likely to be influenced by species- specific social factors. The species for which we calculated the highest within species contacts per period – domestic dogs, cats and spotted hyenas – exhibit a social structure in which they hunt and forage in groups (Macdonald & Carr 1995, Overall et al. 2004,

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Holekamp et al. 1997) Anthropogenic food sources increase group size (Macdonald &

Carr 1995, Crowell-Davis, Curtis & Knowles 2004, Schramme 2014, Yirga et al. 2017) and have disintegrated spotted hyenas’ clan structure in parts of Ethiopia, where hyenas now form groups as large as 150 individuals (Schramme 2014, Yirga et al. 2017). The species for which we calculated the lowest within-species contacts per period – black- backed jackals, African golden wolves, different mongoose species and honey badgers – tend to be more solitary species and scavenge alone, in pairs or in small family groups

(Graw & Manser 2017, Palomares & Delibes 1993, Baker 1998, Jenner, Groombridge &

Funk 2011, Camacho et al. 2016, Kebede 2017, Begg et al. 2005).

Camera traps are an innovative tool that allowed us to indirectly observe terrestrial carnivores, however they are also the source of multiple limitations. First, we were not able identify individuals. This caused an unknown number of repeated animal observations that affected our group size and contacts per period calculations. To accurately calculate these metrics using unique individual sightings, we counted maximum individuals in a single photograph for each 17-hour recording period and averaged this metric across all recording periods at a single site. This analysis might underestimate group size and contacts per period, but we can be confidant that it is an accurate lower bound. This analysis might also obscure true contacts per period if a minority of individuals support the majority of the contacts (Richomee et al. 2006). Once individual recognition becomes possible with camera traps in Ethiopia, it will be important to look at the duration and frequency of individual contacts. Behavioral studies that examine heterogenous mixing within and between populations will also be possible.

Second, we often had difficulty capturing an entire 17-hour recording period at a site.

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Third, detectability was limited to the camera’s field of view. We placed cameras near meat scrap or waste disposal piles where we expected to observe maximum contacts, but placing cameras in areas frequented by social groups can introduce detectability bias as well (Caravaggi et al. 2017). Despite such limitations, similar studies have been able to successfully estimate contact rates in raccoons and red foxes at other communal scavenging sites (Totten et al. 2002, Hirsch et al. 2013, Macdonald et al. 2004).

Conclusions

Our study was one of the first to quantify species composition, abundance, within- species contacts per period, and between-species contacts per period at communal scavenging sites in Ethiopia. Communal scavenging sites are important for pathogen transmission due to a heightened potential for both within- and between-species contacts

(Totten et al. 2002). While the potential for zoonotic spillover is often stressed (Olival et al. 2017, Alexander et al. 2018), our results showed higher within-species contacts per period than between-species contacts per period, similar to results in the Serengeti, North

America, and Europe (Totten et al. 2002, Bohm et al. 2008 and White & Harris 1994,

Lembo et al. 2008). The role that communal scavenging sites play for within-species transmission should not be overlooked. Our study also demonstrates how temporal niche partitioning affects contacts per period and might also influence pathogen transmission.

However, even if species are not directly observed interacting with one another during the same time of day, there is still a risk for transmission of pathogens that persist in the environment. This risk would be greatest for species with close arrival and departure times as was the case with domestic dogs and spotted hyenas. Sequential sharing of food resources between domestic dogs and spotted hyenas at local slaughtering slabs in the

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Serengeti has also been observed and noted for its pathogen transmission potential (Craft et al. 2016). This research indicates that communal scavenging sites are locations where within- and between-species contacts have the potential to shape infectious disease dynamics.

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Chapter 5: Rabies Maintenance Potential Within and Between Terrestrial Carnivore

Species Throughout Ethiopia

Abstract

Intra-and inter-species contact rate estimates identified in chapter 4 were applied to calculations of the basic reproductive number (R0) in order to determine the ability of potential rabies reservoirs to maintain independent transmission of the virus either within a single species or within a 2-species maintenance community. We found that the probability of independent maintenance for intraspecies transmission was highest in domestic dogs followed by spotted hyenas and domestic cats. The probability of interspecies maintenance was highest between spotted hyena and domestic cat populations however probability of maintenance between species was low for all other species pairs. Results emphasize the need to collect more data in order to determine the role of spotted hyena populations as reservoirs for RABV transmission throughout

Ethiopia.

Introduction

The role of wildlife populations as reservoir hosts for rabies virus (RABV) transmission in Ethiopia remains unknown. The virus can infect multiple hosts therefore, multiple variants of the virus are able to simultaneously circulate in different host species or, multiple host species are able to maintain infection of a single variant independently

(Velasco-Villa et al. 2002, Lembo et al. 2007). A different reservoir plays a central role

243 in the maintenance of each viral variant within a given location (Velasco-Villa et al.

2002). It is possible that wildlife populations simply experience spillover from the domestic dog population; a population that is known to maintain rabies transmission throughout Ethiopia (Deressa et al. 2010). It is also possible that either a single wildlife population, or a metacommunity of wildlife populations, can maintain rabies transmission independent of domestic dog populations. The great diversity of rabies reservoirs has made identifying prevention and control strategies increasingly complex (Fisher et al.

2018). Understanding how rabies is maintained in a multi-host system allows appropriate targeting of disease management strategies. If control efforts do not account for transmission in the entire reservoir, then an endless cycle of re-introductions of RABV from wildlife to dogs or vice-versa may continue (Velasco-Villa et al. 2002). Information gained from this study will be critical for selection and targeting of appropriate control methods, especially once the disease is able to be controlled in the domestic dog population.

There are a variety of methods that can be used to identify reservoirs. In a system where no incidence or prevalence data on the disease of interest is present, as is the case with wildlife populations in Ethiopia, one approach is to use disease models. The estimation of the components that make up the transmission parameter (β), which represents the probability of a contact between a susceptible and infected individual and the probability of disease transmission over that contact, is rarely attempted in wildlife populations. This is due to the temporal and spatial resolution at which this epidemiological data must be collected (Real and Biek 2007). However, recent advancements in technology such as camera traps with infrared night vision recording

244 capacity, have increased the ability to monitor populations at levels that can provide the valuable quantitative data needed to estimate the transmission parameter.

Though rabies transmission in wildlife populations cannot currently be tracked in the absence of an outbreak scenario, observation of wildlife contacts in healthy animals using this advanced technology can be applied to models that can then simulate disease dynamics over these conditions. Similar methods have proven effective in studies of rabies transmission in fox populations in Europe and raccoon populations in North

America (Totten et al. 2002, White et al. 1994).

Rabies infection is caused by a negative-sense RNA virus from the genus

Lyssavirus that results in fatal encephalomyelitis once it reaches the brain in all mammals

(Velasco-Villa et al. 2008). The virus can be transmitted in the saliva, tears and cerebrospinal fluid (Hanlon and Childs 2013), with the most common route of transmission being animal bite (World Health Organization 2005b). Every year, over 7 million people receive post-exposure prophylaxis and an estimated 55,000 people die from rabies (Hampson et al. 2009). Policies to manage infection in a target-reservoir system generally contain elements of three different tactics including target control, blocking or barriers, and reservoir control (Haydon et al. 2002). Each tactic requires a progressively increasing level of understanding of reservoir structure (Haydon et al.

2002). Haydon et al. (2002) define a reservoir as one or more epidemiologically connected populations in which a pathogen can be permanently maintained and from which infection is transmitted to the defined target population. Though there is variability in what time period defines maintenance, either indefinite transmission (Haydon et al.

2002) or open-ended transmission until susceptibles run out (Bingham et al. 2005), it can

245 generally be agreed upon that it is dependent on the ability of the reservoir to replicate, shed and transmit virus efficiently to conspecifics over an extended period of time

(Bingham et al. 2005). Maintenance is dependent on a continuum of both within-and between species transmission (Fenton and Pedersen 2005, Viana et al. 2014).

Throughout Africa, domestic dogs serve as the principal reservoir for canine rabies (Johnson et al. 2010) while wildlife such as mongoose, bats, jackals and foxes may harbor other variants (Sillero-Zubiri et al.1996, Lembo et al. 2008). In South Africa and

Zimbabwe, the mongoose harbors an independent strain of the virus while the black- backed jackal and bat-eared fox are able to support transmission independent of domestic dogs in particular geographical areas as long as certain ecological conditions exist. (Nel et al. 1993b, Sabeta et al. 2003, Sabeta et al. 2007, Zulu et al. 2009). In contrast, domestic dogs have been found to be the sole reservoir of rabies in Tanzania and Kenya

(Cleaveland and Dye 1995, Lembo et al. 2008, Prager et al. 2012). In Ethiopia, one of the most rabies-affected countries in the world with a national annual incidence rate of

12/100,000 population rabies exposures and 1.6/100,000 population rabies deaths as of the year 2013 (Deressa et al. 2013), the only known reservoir is the domestic dog

(Deressa et al. 2010, Johnson et al. 2010). Outside of research on the Ethiopian wolf

(Sillero-Zubirie et al. 1996), what is known comes predominantly from passive surveillance showing sporadic cases in various wildlife species, especially the spotted hyena (Crocuta crocuta) and the African golden wolf (Canis anthus) (previously identified as common jackal) (Yimer et al. 2002, Ali et al. 2011). Despite vaccination campaigns and population control efforts for domestic dogs in targeted areas, such as the

Bale Mountains (Randall et al. 2006), rabies remains endemic in Ethiopia suggesting

246 wildlife may play a role in rabies persistence or that there is re-introduction of dog- maintained rabies virus variants from neighboring regions.

Throughout the world, most of the major wildlife reservoir hosts of rabies are opportunistic species that live at relatively high densities in agricultural areas or close to human settlements (Cleaveland and Dye 1995). As a country with nearly 102 million people where agriculture is the main source of income (United Nations Statistics Division

2016) and there are stable populations of such opportunistic species, Ethiopia has great potential for such transmission cycles to be present. One of these opportunistic species is the spotted hyena (Crocutta crocutta). The spotted hyena is of special interest because this species is known to exist at very high densities in certain parts of the country in addition to being able to survive almost entirely off of anthropogenic food sources (Yirga et al. 2013). It has even been found that as a result, this species has lost its clan structure in certain parts of the country allowing them to exist as large groups with little social structure resulting in more dense populations in smaller areas (Schramme 2015). Hyenas in Ethiopia are also known to move constantly in and out of large cities and towns during the night. As a result, they come in to close contact with dogs guarding households and thus serve as vehicles for rabies transmission from rural to urban areas and vice versa

(Mebatsion et al. 1992).

The objective of this study is to apply contact rates identified at communal foraging sites throughout Ethiopia (Binkley 2018 unpublished) to mathematical expressions that will help determine maintenance potential within and between domestic dog (Canis lupis familaries), domestic cat (Felis catus), spotted hyena (Crocutta crocutta), mongoose (family Herpestidae), jackal (Canis sp.) and honey badger

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(Mellivora capensis) populations. Results will be the first step towards identification of the complete reservoir in addition to domestic dogs throughout Ethiopia.

Methods

Site Selection

Sites were selected from existing contact data acquired from camera traps reported in Binkley 2018, unpublished. For this study, sites included Addis Ababa

(Oromia region), Bale (Oromia region), Awash (Afar region) and Awassa (Southern

Nations Nationalities and Peoples’ region) (Figure 17). The central slaughter plant for each city was identified and one camera trap was placed at each slaughter plant. An additional camera trap was placed at the central waste disposal facility in Awassa.

Slaughter plants had an average area of (4,058.7m2) (Table 15). The waste disposal facility had a total area of 13,767m2.

Species Selection

Domestic dog (Canis lupis familaries), domestic cat (Felis catus), spotted hyena

(Crocutta crocutta), mongoose (family Herpestidae), jackal (Canis sp.) and honey badger

(Mellivora capensis) populations were selected because they are all known to become infected with the rabies virus, have stable populations throughout Ethiopia, have overlapping habitat ranges and are known to co-occur throughout the country (IUCN Red

List 2018, Laura Binkley 2018 unpublished).

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Awash National Park Addis Ababa

Bale Mountains Awassa

Figure 17. Camera Trap Survey Locations

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Table 16. Area of Selected Study Sites

Study Site Area m2 % Area % Area Total Captured by Captured by recording Camera Camera time (min) Trap Day Trap Night

Addis Ababa Slaughter 3154.61 20.20 2.25 13070 Plant

Bale Slaughter Plant 4502.49 14.20 1.57 3689

Awash Slaughter Plant 4241.6 15.00 1.67 4633

Awassa Slaughter Plant 4336.31 14.70 1.63 2970

Awassa Waste Disposal 13767 4.60 0.51 1544 Facility

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Modeling rabies transmission within species

Rabies transmission was represented using the susceptible, exposed, infectious, removed (SEIR) model for transmission of infectious diseases (Anderson & May 1991,

Keeling & Rohani 2008). Susceptible (S) individuals are those who never experienced rabies infection. Exposed (E) individuals had contact with an infectious individual and contracted rabies but are not yet capable of infecting other animals. Infectious (I) individuals had contracted rabies virus and are capable of infecting other animals given adequate contact. Removed (R) animals are those who die due to rabies infection after the infectious period. In order to parameterize the model following Begon et al. (2002), transmission was represented as a function of the rate at which any individuals in the population contact each other (푐), the proportion (푝) of the population that is infectious, and the combined probability (휈) of bite with the probability of transmission over that bite (Begon et al. 2002). Let 퐸푡 represent the number of new rabies infections that occur in timestep 푡, such that

퐸푡 = 푐푝휈푆푡−1 . (1)

In order to determine if each species could maintain endemic rabies entirely through within species contacts, 푅0, which is the number of secondary cases produced by each infectious individual in a totally susceptible population, was necessary. When 푅0 ≥

1, the number of secondary cases greater than or equal to the number of primary cases, and transmission is maintained. When 푅0 < 1, the number of secondary cases is less than the number of primary cases, and transmission is not maintained. There are two steps to quantifying 푅0: first, a mathematical expression for the term using our parameterization

251 of the SEIR model must be derived; second, the expression for 푅0 must be parameterized using the contacts determined from the camera trap data (Table 16) and disease parameters determined from the literature (Table 17).

A mathematical expression for 푅0 was derived using the next-generation method

(Diekmann, Heesterbeek & Metz 1990; van den Driessche & Watmough 2002;

Heffernan, Smith & Wahl 2005). Parameterization of the SEIR equations with 푐, 푝, and 휈

(Begon et al. 2002), determined that

푐휈 푅 = . (2) 0 훾

For the full set of equations and the step-by-step derivation, see Appendix B. In order to determine if each species could maintain rabies, it was first assumed that R0 = 1 and applied previously calculated contact rates for domestic dogs at the Addis Ababa site to determine the (v) threshold value. The threshold for domestic dogs in Addis Ababa was selected because dogs can maintain independent transmission. As an upper bound estimate for (v), we applied the probability of rabies given bite in domestic dogs reported in Hampson et al. (2009) (0.49) multiplied by a 0.50 probability of bite to arrive at 0.25.

This provided us with a range of plausible (v) values for our next step. Our next step was to solve for R0 by applying previously calculated within-species contact rates for each species at each site and varying the (v) parameter at, above and below the (v) threshold for maintenance in domestic dogs in Addis Ababa to determine which species could maintain transmission across a range of transmission (v) values.

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Table 17. Contact Rates Using 1 Day Time Steps for Each Species at Each Site/

Ci,i Parameters

Site Spotted Dog Cat Mongoose Jackal Honey Hyena Badger

Addis 11.77 5.77 0.23 0.08 0.23 0 Ababa

Slaughter Plant

Bale 0 216 0 0 0 0 Slaughter

Plant Awash 0.4 1.4 2.8 0 0 0.2

Slaughter Plant

Awassa 20.33 3.33 4 0 0 0 Slaughter

Plant Awassa 5 0.667 0 0 0 0

Waste Disposal

Facility

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Table 18. Parameterization of the SEIR to Represent Rabies Transmission in

Ethiopia.

Parameter Definition Value Reference Y Per capita rate at 3 days Hampson et al. which infectious (per 1-day time 2009 individuals die step = 1/3). from rabies P Probability of 0.49 Hampson et al. rabies|bite rabies given bite 2009 in domestic dogs in Tanzania Ν Combined 0.01-0.25 probability of bite with the probability of transmission over that bite

Determining if pairs of wildlife species could comprise a reservoir for rabies

maintenance

If a species cannot maintain rabies transmission through within species contacts, it is possible that a pair of species might act as a reservoir that can maintain rabies transmission via within and between species contacts. Consider two species – species 푖 and species 푗 – each with a proportion of infected animals: 푝푖 and 푝푗. The 푐푖,푖 parameter was defined as the within species contact rate; now, let 푐푖,푗 represent the rate of contact between species 푖 and species 푗 (Table 18). Likewise, let 휈푖,푗 represent the probability of

254 a bite when there is between species contact. Let 퐸푖,푡 represent number of new rabies infections that occur in species 푖 at timestep 푡. Assuming that a pair of species might maintain rabies via within and between species transmission, it follows that

퐸푖,푡 = (푐푖,푖푝푖휈푖,푖 + 푐푖,푗 푝푗휈푖,푗) 푆푖,푡−1. (3)

A mathematical expression for 푅0 was derived for the case of two species and it was assumed that transmission can occur via within and between species contacts using the next-generation method (Diekmann, Heesterbeek & Metz 1990; van den Driessche &

Watmough 2002; Heffernan, Smith & Wahl 2005). The SEIR equations were parameterized with 푐푖,푖, 푐푖,푗, 푝푖, 푝푗, v (Begon et al. 2002) and it was determined that

(4)

For the full set of equations and the step-by-step derivation, see Appendix B. In order to determine if each pair of species could maintain rabies, the same range of (v) values used in the within-species maintenance calculations were applied to calculations of R0 between species. It was assumed that (v), again the probability of bite and the probability of transmission over that bite, for within species transmission was comparable to between species transmission. The R0 was then solved for by applying previously calculated between species contact rates for each pair of species at each site while varying the (v) parameter at, above and below the (v) transmission threshold for maintenance in domestic dogs in Addis Ababa to determine which species could maintain transmission across a range of transmission values. Because the probability of contact (c) is not directly linked

255 mathematically to (v), the expression will show that maintenance can still be achieved between species if the (v) transmission value is high, even if there is no contact between species. This is not consistent with the ecology of transmission therefore, when between species contacts do not occur, the R0 value remains zero.

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Table 19. Contact Rates Using 1-Day Time Steps for Each Pair of Species at Each Site/ Ci,j Parameters

Site Dog- Hyena- Hyena- Hyena- Jackal- Mon- Jackal- Cat Cat Jackal Mongoose Mongoose goose Cat -Cat

Addis 0.23 0.31 0.08 0.69 0.08 0.00 0.00 Ababa Slaughter Plant Bale 0.00 0.00 0.00 0.00 0.00 0.25 0.00 Slaughter Plant Awash 2.00 1.00 0.60 0.00 0.00 0.20 0.20 Slaughter Plant Awassa 0.00 16.00 0.00 0.00 0.00 0.00 0.00 Slaughter Plant Awassa 0.00 2.00 0.00 0.00 0.00 0.00 0.00 Waste Disposal Facility

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Results

Calculations of the basic reproductive number at the (v) threshold for maintenance in domestic dogs of Addis Ababa (v = 0.06) show that the probability of maintaining the rabies virus through intraspecies transmission is highest in spotted hyena and domestic dog populations at two sites. Spotted hyenas were capable of maintaining transmission at the Addis Ababa and Awassa slaughter plant sites while domestic dogs were able to maintain transmission at the Addis Ababa and Bale slaughter plant sites.

When lowering the (v) transmission parameter below the value for maintenance in domestic dog populations of Addis Ababa (lowered to v = 0.03), maintenance of transmission is still possible at multiple sites in spotted hyena populations (Addis Ababa and Awassa slaughter plants) and at one site in domestic dog populations (Bale slaughter plant).

Increasing the (v) transmission parameter above the value for maintenance in domestic dog populations of Addis Ababa (raised to v =0.1), shows that maintenance of transmission can be reached at 3 sites for both spotted hyena (Addis Ababa and Awassa slaughter plants, Awassa waste disposal facility) and domestic dog populations (Addis

Ababa, Bale and Awassa slaughter plants) and at one site for domestic cat populations

(Awassa slaughter plant).

At the upper bound (v) value of 0.25, maintenance is achieved by domestic dogs at 4 sites (Addis Ababa, Bale, Awash and Awassa slaughter plants), spotted hyenas at 3 sites (Addis Ababa and Awassa slaughter plants, Awassa waste disposal facility) and domestic cats at 2 sites (Awash and Awassa slaughter plants).

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Though domestic dog and spotted hyena populations appear to be most evenly distributed across sites, there is still a great deal of site-to-site variability for calculations of R0. Overall, highest R0 values were achieved by domestic dog populations at the Bale slaughter plant site. Maintenance was achieved by the most species at the Awassa slaughter plant site. See results for intraspecies R0 calculations in Table 19.

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Table 20. Intraspecies R0

R0 when v = 0.03 Species Site Spotted Dog Cat Jackal Mongoose Honey Hyena Badger Addis Ababa 1.07 0.52 0.02 0.02 0.01 0 Bale 0 19.44 0 0 0 0 Awash 0.04 0.13 0.25 0 0 0.02 Awassa Slaughter 1.85 0.3 0.36 0 0 0 Awassa Waste 0.45 0.06 0 0 0 0

R0 when v = 0.06 *v-threshold value for maintenance in domestics dogs of Addis Ababa Species Site Spotted Dog Cat Jackal Mongoose Honey Hyena Badger Addis Ababa 2.14 1.04 0.04 0.04 0.01 0 Bale 0 38.88 0 0 0 0 Awash 0.07 0.25 0.5 0 0 0.03 Awassa Slaughter 3.7 0.6 0.72 0 0 0 Awassa Waste 0.91 0.12 0 0 0 0

R0 when v = 0.10 Species Site Spotted Dog Cat Jackal Mongoose Honey Hyena Badger Addis Ababa 3.53 1.73 0.07 0.069 0.023 0 Bale 0.00 64.80 0.00 0 0 0 Awash 0.12 0.42 0.84 0 0 0.06 Awassa Slaughter 6.10 1.00 1.20 0 0 0 Awassa Waste 1.50 0.20 0.00 0 0 0

R0 when v = 0.25 Species Site Spotted Dog Cat Jackal Mongoose Honey Hyena Badger Addis Ababa 8.83 4.33 0.17 0.17 0.06 0 Bale 0 162 0 0 0 0 Awash 0.3 1.05 2.1 0 0 0.15 Awassa Slaughter 15.25 2.5 3 0 0 0 Awassa Waste 3.75 0.5 0 0 0 0

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Interspecies calculations of the basic reproductive number at the (v) threshold for maintenance in domestic dogs of Addis Ababa (v = 0.06) show that maintenance of the rabies virus could be reached between spotted hyena and domestic cat populations at 3 sites (Addis Ababa and Awassa slaughter plants, Awassa waste disposal facility), spotted hyena and jackal populations at 1 site (Addis Ababa slaughter plant), spotted hyenas and mongoose at 1 site (Addis Ababa slaughter plant) and between domestic dogs and cats at

1 site (Addis Ababa slaughter plant).

When lowering the (v) transmission parameter below the value for maintenance in domestic dog populations of Addis Ababa (lowered to v = 0.03), maintenance can still be achieved at 2 sites between spotted hyena and domestic cat populations (Addis Ababa and Awassa slaughter plants), 1 site between spotted hyena and jackal populations

(Addis Ababa slaughter plant) and 1 site between spotted hyena and mongoose populations (Addis Ababa slaughter plant). Maintenance of transmission between domestic dogs and cats would no longer be achieved.

Increasing the (v) transmission parameter above the value for maintenance in domestic dog populations of Addis Ababa (raised to v = 0.1) shows that maintenance is reached between spotted hyenas and domestic cats at 4 sites (Addis Ababa, Awash and

Awassa slaughter plants, Awassa waste disposal facility), between domestic dogs and cats at 2 sites (Addis Ababa and Awash slaughter plants), between spotted hyena and jackal at 1 site (Addis Ababa slaughter plant) and between spotted hyena and mongoose populations at 1 site (Addis Ababa slaughter plant).

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At the upper bound (v) value of 0.25, maintenance is achieved between spotted hyena and domestic cats at 4 sites (Addis Ababa, Awash and Awassa slaughter plants,

Awassa waste disposal facility), domestic dogs and cats at 2 sites (Addis Ababa and

Awash slaughter plants), spotted hyenas and jackals at 1 site (Addis Ababa slaughter plant), spotted hyenas and mongoose at 1 site (Addis Ababa), mongoose and domestic cat populations at 1 site (Awash slaughter plant) and between domestic cat and jackal populations at 1 site (Awash slaughter plant). Overall, highest R0 values were found between spotted hyena and domestic cat populations at the Awassa slaughter plant site.

Maintenance was consistently achieved by the most species pairs at the Addis Ababa site.

See results for intespecies R0 calculations in Table 20.

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Table 21. Interspecies R0

R0 at v = 0.03 Species Site Dog- Hyena- Hyena- Hyena- Jackal- Mongoose- Jackal- Cat Cat Jackal Mong. Mong. Cat Cat Addis Ababa 0.55 1.09 1.09 1.08 0.03 0 0 Bale 0 0 0 0 0 0.02 0 Awash 0.45 0.32 0.08 0 0 0.26 0.26 Awassa Slaughter 0 2.93 0 0 0 0 0 Awassa Waste 0 0.52 0 0 0 0 0

R0 at v = 0.06 *v-threshold value for maintenance in domestics dogs of Addis Ababa Species Site Dog- Hyena- Hyena- Hyena- Jackal- Mongoose- Jackal- Cat Cat Jackal Mong. Mong. Cat Cat Addis Ababa 1.09 2.18 2.18 2.16 0.06 0 0 Bale 0 0 0 0 0 0.05 0 Awash 0.91 0.63 0.15 0 0 0.51 0.51 Awassa Slaughter 0 5.87 0 0 0 0 0 Awassa Waste 0 1.04 0 0 0 0 0

R0 at v = 0.10 Species Site Dog- Hyena- Hyena- Hyena- Jackal- Mongoose- Jackal- Cat Cat Jackal Mong. Mong. Cat Cat Addis Ababa 1.82 3.64 3.64 3.60 0.1 0 0 Bale 0 0 0 0 0 0.08 0 Awash 1.52 1.06 0.25 0 0 0.85 0.85 Awassa Slaughter 0 9.78 0 0 0 0 0 Awassa Waste 0 1.73 0 0 0 0 0 R0 at v = 0.25 Species Site Dog- Hyena- Hyena- Hyena- Jackal- Mongoose- Jackal- Cat Cat Jackal Mong. Mong. Cat Cat Addis Ababa 4.55 9.1 9.09 9.01 0.25 0 0 Bale 0 0 0 0 0 0.19 0 Awash 3.79 2.64 0.63 0.3 0 2.13 2.13 Awassa Slaughter 0 24.44 0 0 0 0 0 Awassa Waste 0.5 4.32 0 0 0 0 0

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Discussion

Calculations of R0 showed that the intraspecies potential for maintenance of rabies virus transmission at communal foraging sites throughout Ethiopia is highest in domestic dog followed by spotted hyena and domestic cat populations, respectively. The interspecies potential for maintenance of rabies virus transmission is low overall, with highest values observed between spotted hyena and domestic cat populations. Results suggest that the role of intraspecies transmission of the rabies virus plays a greater role than interspecies transmission.

Limitations

Though transmission potential is expected to be greater at communal foraging sites and thus R0 values may be higher than what would be observed outside of these sites, some of this may be counterbalanced by use of conservative estimate for the probability of bite upon contact (50%) along with the combined probability of bite and transmission over that bite (v) which ranged from 3-25%. If a rabid animal contacts another individual, there is a ≥ 50% probability that the rabid animal will bite the individual based on aggression and increased frequency of biting behavior that are often exhibited in rabid animals that experience the furious form of the disease (Anderson et al.

1981). Though development of furious vs. paralytic rabies is species dependent, it has been reported that roughly 80% of cases exhibit the furious, or encephalitic, form of the disease (Lueng et al. 2007).

There was a great deal of site-to-site variability in R0 calculations suggesting that transmission is very site-specific. This is logical considering differences in populations

264 sizes, resource availability and distribution and overall ecology across the country. This highlights the need for further data collection in different ecosystems.

Host-specific biological factors and social factors were not incorporated in the models because many of these factors remain unknown. For example, the models assume that the (v) transmission parameter was comparable across species as well is within and between species. However, some species may show more resistance to infection than others in which case, there would be differences in the (v) parameter. Data regarding the ability of a species to resist rabies infection is scarce. Similarly, the same (y) parameter value was applied to all species however, there may be variability the amount of time that an animal is infectious. These issues can be accounted for by using multiple (v) and (y) parameters once these biological factors become known.

Though seasonality was not incorporated in this study, the effects of seasonality may be minimal for some species. For example, it has been shown that spotted hyena clans do not fluctuate by season in the same way that packs of wild dogs do (Mills 1993).

They do not experience a seasonal birth pulse and mortality rates are lower on average than in wild dogs (Mills 1993).

Intraspecies R0

At lowest (v) parameter values for intraspecies transmission, maintenance is achieved at the most sites in spotted hyena populations followed by domestic dog populations. As the (v) value is increased above the threshold for maintenance in domestic dog populations of Addis Ababa, maintenance is reached in domestic cat populations at several sites (maximum of 2 sites). At the maximum (v) value, transmission is able to be maintained at the most sites in domestic dogs followed by

265 spotted hyenas and domestic cats, respectively. Though transmission potential is high in both spotted hyena and domestic dog populations overall, domestic dogs achieve maintenance of the virus at an additional site when the (v) value is increased to the upper bound estimate because they are present at the Bale slaughter plant site where spotted hyena populations were not observed.

Contact rates for domestic dogs at the Bale slaughter plant were unusually high.

This may be due to the fact that there appears to be less competition from other species at this site, where the fewest number of different species were observed. Though the greatest diversity of species was observed at the Addis Ababa slaughter plant site, the greatest number of species to achieve intraspecies maintenance was observed at the

Awassa site. This may be due to a combination of ecological factors. For example, there may be greater species diversity combined with higher population sizes as a result of an abundance of water bodies, lowland habitat and moderate temperatures (Government of

Ethiopia 2018) at the Awassa site. Addis Ababa is very urban with an estimated human population density of at least 2.7 million (Government of Ethiopia, 2007) that is also located in the highlands of Ethiopia between 2,200 and 2,500m above sea level

(Government of Ethiopia 2018). Though many species may interact here, population sizes of some species may be limited by space or by competition from other species thus limiting maintenance potential. In the Serengeti, Lembo et al. (2007) propose that higher species diversity prevents the ability of any single population to reach high enough densities and thus achieve high enough contact rates to independently maintain rabies infection. It appears that this is true unless recourses are abundant. This is also consistent

266 with another ecological principle that within a community, a few species tend to make up the majority of the individuals (Verberk 2011) and thus the majority of the interactions.

There is strong support regarding results about the potential for spotted hyenas to maintain independent transmission of the rabies virus based off of contact rates and the basic biology of the disease. Spotted hyenas were estimated to be able to maintain transmission at multiple sites across the entire range of v parameter values. They were even able to maintain independent transmission when the probability of bite and transmission over that bite (v) was only 3 out of 100, which is a very conservative estimate. Results are also consistent with spotted hyena population estimates which have been reported to reach as high as 0.8 hyena/km2 in some parts of the country (Yirga et al.

2017). As previously mentioned, this is likely due to an abundance of food provided by human waste that has resulted in the loss of social structure in the species which in turn has allowed them to gather in large numbers without conflict (Schramme 2015).

Though there is great potential for this species to maintain independent rabies transmission, this does not take into account host-specific factors such as immunity or resistance to the disease. East et al. (2001) reported finding that spotted hyenas in the

Serengeti were able to obtain an asymptomatic carrier state. However, these results have never been able to be repeated and no virus was actually isolated from the samples as results were based on serology. Additionally, Lembo et al. (2007) note that they have found that clinical signs of hyenas infected with the canine rabies variant are typical and that rabies morbidity and mortality has been reported in hyenas in other parts of Africa.

According to them, there is no doubt that Serengeti hyenas can die when infected with dog rabies and that rabid hyenas pose a severe risk to humans and other mammals

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(Lembo et al. 2007). Though an asymptomatic carrier state may not exist, it is possible that spotted hyenas in Ethiopia are more resistant to infection for unknown biological reasons. It has been shown that their immune systems are able to cope far better with bacteria and other diseases than many other sympatric carnivores (Smith and Holekamp

2010). For example, they have been known to feed on anthrax-infested carcasses without any detrimental consequences and are able to digest every part of an animal except hair and hooves (Yirga et al. 2015a). In North America, it has been proposed that Virginia opossums (Didelphis virginiana) are more resistant to rabies than other mammals because their body temperature (34.4° to 36.1° C [94° to 97°F]) is too low to harbor the virus (Diana et al. 2015). Though the body temperature of spotted hyenas is reported to be around 36.1°C (96.98°F) (Flies 2012), there may be other unknown biological factors that could contribute to the susceptibility of spotted hyenas to the virus. For example, they are large animals with very thick hides. It may be more difficult for smaller mammals, such as mongoose, to bite spotted hyenas and transfer the infection. These potential host-specific biological factors should be explored in the future to improve understanding of the role of spotted hyenas in rabies transmission cycles.

Interspecies R0

At lowest (v) values, transmission is maintained at the most sites between spotted hyena and domestic cat populations followed by spotted hyenas and jackals and then spotted hyenas and mongoose, respectively. When (v) values are raised, maintenance is then achieved between domestic dog and cat populations at several sites (maximum of 2 sites). At the maximum (v) value, transmission is still maintained at the most sites between spotted hyena and domestic cat populations followed by domestic dog and cat

268 populations, spotted hyena and jackal populations and spotted hyena and mongoose populations. Maintenance of the RABV is attained among mongoose and domestic cat populations as well among jackal and domestic cat populations at 1 site.

Overall, highest R0 values were found between spotted hyena and domestic cat populations at the Awassa slaughter plant site. Interestingly, spotted hyenas and domestic cats are more closely related to each other than to domestic dogs, both coming from the suborder Feliformia (Flynn et al. 2005). Maintenance was consistently achieved by the most species pairs at the Addis Ababa site. As previously mentioned, though populations of certain species may be lower in Addis Ababa than in Awassa due to limited space or increased competition for resources, the greater diversity of species provides more opportunity for between species maintenance. If populations of a single species are low in

Addis Ababa but diversity is high, it is still possible for that species to achieve maintenance in combination with one or more species as a maintenance community.

My results showed that interspecies interactions including spotted hyenas often resulted in maintenance, even when interspecies contact rates were relatively low. For example, the interspecies contact rate between spotted hyenas and jackals was 0.08 contacts per recording period at the Addis Ababa site. However, the intraspecies contact rate in the spotted hyena population was very high at this site (11.77 contacts per recording period) and there were still some intraspecies contacts in the jackal population as well (0.23 contacts per recording period). It appears that due to the high intraspecies contact rates in the spotted hyena population combined with some intraspecies contacts in the jackal population, even a small number of interspecies contacts can allow the transmission cycle to take off. This scenario has been described in continuums created by

269 both Fenton and Pedersen (2005) as well as Viana et al. (2014) with the x-axis being represented by either interspecies transmission or the force of infection from another species and the y-axis being represented by either intraspecies transmission or R0. Both continuums show that maintenance can be reached if net intraspecies contact rate is high and interspecies contacts are low.

It is important to consider biological factors when examining interspecies interactions as well. Though maintenance was achieved between spotted hyena and domestic cat populations, it is unlikely that a domestic cat would survive a bite from a hyena and then continue transmission of the virus. Therefore, the force of infection exerted on spotted hyenas by domestic cats may be greater than the force of infection exerted on domestic cats by spotted hyenas. Similarly, though maintenance was achieved between spotted hyena and mongoose populations, it is unlikely that a mongoose would survive a bite from a hyena or bite and transmit the virus to the hyena. However, domestic cats are observed with canine rabies which they most likely acquire from dogs therefore, bites from larger carnivores are not always fatal and can result in continuation of the transmission cycle. Similarly, juveniles are much smaller than adults and may become more easily infected by other smaller mammals thus continuing transmission cycles. Differences in the (v) parameter based on biological factors can be accounted for in future models by using two (v) transmission parameter values as opposed to one.

Conclusion

Results from this study suggest that, in addition to domestic dogs, the spotted hyena population in Ethiopia may play significant role as part of the reservoir for rabies transmission throughout the county. Domestic cats seem to play a role as part of the

270 reservoir as well however, to a lesser extent. Additionally, high intraspecies transmission rates in these species may increase the ability of a maintenance community to exist among different species. Before targeting control efforts towards these populations however, more data needs to be gathered on host-specific biological and social factors such as resistance factors and heterogeneity in transmission both within and between species. The investigation of contact and transmission at sites other than communal foraging sites and throughout different ecosystems will also be important to identify the role of more solitary species such as jackals and foxes and to provide more site-specific data. Examination of contact and transmission rates over multiple seasons can help identify seasonal factors in species that may experience such variation. Ultimately, once this data becomes available, creation of a model that can examine the ability of more than two species to act as a maintenance community will be necessary. This study provides the first steps in the identification of terrestrial wild carnivore species as part of the reservoir for rabies transmission throughout Ethiopia.

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Chapter 6: Conclusions

The diversity of rabies reservoirs has introduced many challenges to prevention and control efforts (Fisher et al. 2018). Knowledge regarding how rabies is maintained in such a multi-host system is essential for prevention and control to allow appropriate targeting of disease management strategies. If the entire reservoir is not accounted for when applying prevention and control efforts, then an endless cycle of re-introductions of

RABV from wildlife to dogs or vice-versa may continue (Velasco-Villa et al. 2002).

Here, I sought to obtain knowledge about the existing reservoir for RABV in Ethiopia by identifying transmission cycles within domestic dog populations as well as alternative cycles in other mammalian species.

In chapter 3, I identifed a homogenously distributed, dog epizootic RABV variant circulating throughout Ethiopia suggesting the rabies virus is is capable of spreading throughout the country with no apparent boundaries. I also identified a RABV variant circulating in side-striped jackals in the southern Ethiopia that showed 3.3% divergence from the canine variant. Lastly, I was able to show that circulation of RABVs in Ethiopia occurs on a within-country scale with no current or ongoing introductions from other

African countries. In chapters 4 and 5, I explored alternative methods to examine existing transmission cycles, specifically searching for independent maintenance potential within populations as well as the existence of maintenance communities between populations. In chapter 4, I used camera traps to identify intraspecies and interspecies contact rates at

272 communal foraging sites throughout Ethiopia. I found that though contacts were site specific, overall intraspecies contact rates were very high in domestic dogs as expected, but also in spotted hyena populations which has not been previously reported. Domestic cats also showed moderately high contact rates. Interspecies contact rates were high between domestic cat and spotted hyena populations but low in other species indicating intraspecies transmission occurs more frequently than interspecies transmission at communal foraging sites throughout the country

In chapter 5, I applied intraspecies and interspecies contact rates to calculations of the basic reproductive number (R0) in order to determine maintenance potential within and between populations identified as high-risk RABV transmitters in chapter 4. I found that, in agreement with results from contact rates, intraspecies maintenance potential was highest in domestic dog and spotted hyena populations. Moderate potential was observed in domestic cat populations. Interspecies maintenance was able to be achieved between spotted hyena and domestic cats, jackals and mongoose as well as between domestic dogs and cats at moderate transmission parameter values. I propose that maintenance is able to be achieved between populations interacting with spotted hyena populations as long as intraspecific contact rates at a given site within the spotted hyena population are high along with at least some intraspecies contact in the second interacting species combined with minimal between species contact rates.

This work highlights the need to instate legislation that will limit the movement of domestic dogs throughout the country in order to prevent spreading of the RABV and to promote Ethiopian wolf conservation. This legislation will be especially important in conservation areas, such as the Bale mountains, where movement of domestic dogs in

273 and out of protected areas is not regulated. It also emphasizes the need for more intensive surveillance in wildlife populations, especially side-striped jackal and spotted hyena populations. Results were able to provide important baseline information for ongoing prevention and control efforts as well as the first steps in identification of the complete reservoir for RABV transmission in Ethiopia.

Future molecular studies may benefit from more fine-scale molecular analysis using whole genome sequencing to identify geographical variants at more local scales.

Future ecological studies should focus camera trapping efforts at a greater diversity of locations, including sites that are not communal foraging sites to examine contacts in more solitary species, and observe different behaviors that may affect disease transmission (e.g. licking, biting, denning). Efforts should include further investigation into the potential for spotted hyena populations to maintain rabies transmission. Further investigation into transmission within bat and mongoose populations will also be important based on the significance of these species in transmission cycles throughout other parts of the world. Future disease modeling studies will benefit from creating a model that examines the ability of more than two species to act as a maintenance community. This will help determine the size of the reservoir if a maintenance community exists. Finally, incorporation of within-and between-species heterogeneity to future disease models will be important when individual recognition becomes possible either with camera traps or other methods.

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Appendix A. Intraspecies and Interspecies Contact Raw Data (Chapter 4)

Table 22. Minutes Recording per Period at Each Site.

Period Addis Goba Awash Awassa Awassa Ababa Slaughter Slaughter Slaughter Waste Slaughter Plant Plant Plant Disposal Plant Facility 1 1020 990 968 990 629 2 1020 990 980 990 686 3 1020 990 990 990 229 4 1020 719 983 5 1020 712

6 1020 7 1020 8 1020 9 990 10 990 11 990 12 990 13 950 Total 13070.00 3689 4633 2970 1544

Average 1005.38 922.25 926.60 990 514.67

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Table 23. Maximum Number of Individuals Observed per Recording Period

Addis Ababa Slaughter Plant.

Period Dog Cat Spotted African Mongoose Hyena Golden Wolf 1 1 0 4 1 1 2 2 1 2 0 0 3 4 1 7 0 1 4 3 1 1 0 0 5 2 1 3 0 0 6 4 1 4 0 0 7 4 1 4 0 1 8 4 0 0 0 1 9 6 0 9 3 2 10 6 3 11 1 1 11 3 0 4 0 1 12 5 1 3 1 1 13 3 1 5 1 1 Total 47 11 57 7 10 Average 3.62 0.85 4.38 0.54 0.77 Standard 1.50 0.80 3.07 0.88 0.60 Deviation Group Size 6 3 11 3 2

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Table 24. Maximum Number of Individuals Observed per Recording Period Goba

Slaughter Plant.

Period Dog Cat Mongoose

1 23 1 1 2 11 1 0 3 33 1 1 4 8 1 0 Total 75 4 2 Average 18.75 1 0.5 Standard 11.50 0.00 0.58 Deviation Group Size 33 1 1

Table 25. Maximum Number of Individuals Observed per Recording Period

Awash Slaughter Plant.

Period Dog Cat Spotted Black- Mongoose Honey Hyena backed Badger Jackal 1 1 2 2 1 0 2 2 3 3 1 1 1 0 3 2 3 1 1 1 0 4 3 4 2 1 0 0 5 1 2 1 0 1 0

Total 10 14 7 4 3 2 Average 2.00 2.80 1.40 0.80 0.60 0.40

Standard 1.00 0.84 0.55 0.45 0.55 0.89 Deviation Group size 3 4 2 1 1 2

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Table 26. Maximum Number of Individuals Observed per Recording Period

Awassa Slaughter Plant.

Period Dog Cat Spotted Hyena 1 2 3 10 2 3 4 5 3 4 3 4 Total 9.00 10.00 19.00 Average 3.00 3.33 6.33 Standard Deviation 1.00 0.58 3.21 Group Size 4 4 10

Table 27. Maximum Number of Individuals Observed per Recording Period

Awassa Waste Disposal.

Dog Cat Spotted Hyena Period 1 1 0 4 Period 2 0 1 4 Period 3 2 1 7 Total 3.00 2.00 15.00 Average 1.00 0.67 5.00 Standard Deviation 1.00 0.58 1.73 Group Size 2 1 7

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Table 28. Maximum Group Size Across Periods by Species and Site.

Dog Cat Spotted Mongoose Black- African Honey Total Average St. Hyena backed Golden Badger Dev Jackal Wolf Addis Ababa 6 3 11 2 N/A 3 N/A 25.00 5.00 3.67 Slaughter Plant Goba 33 1 N/A 1 N/A N/A N/A 35.00 11.67 18.48 Slaughter Plant Awash 3 4 2 1 1 N/A 2 13.00 2.17 1.17 Slaughter Plant Awassa 4 4 10 N/A N/A N/A N/A 18.00 6.00 3.46 Slaughter Plant Awassa 2 1 7 N/A N/A N/A N/A 10.00 3.33 3.21 Waste Disposal Total 48.00 13.00 30.00 4.00 1 3 2 Average 9.60 2.60 7.50 1.33 1 3 2 Std. Dev. 13.16 1.52 4.04 0.58 N/A N/A N/A

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Table 29. Average of Maximum Number of Individuals Across Recording Periods by Species and Site.

Dog Cat Spotted Mongoose Black- African Honey Total Average St. Hyena backed Golden Badger Dev. Jackal Wolf Addis Ababa 3.62 0.85 4.38 0.77 N/A 0.54 N/A 10.16 2.03 1.82 Slaughter Plant Bale 18.75 1 N/A 0.5 N/A N/A N/A 20.25 6.75 10.40 Slaughter Plant Awash 2 2.8 1.4 0.6 0.8 N/A 0.4 8.00 1.33 0.93 Slaughter Plant Awassa 3 3.33 6.33 N/A N/A N/A N/A 12.66 4.22 1.83 Slaughter Plant Awassa 1 0.67 5 N/A N/A N/A N/A 6.67 2.22 2.41 Waste Disposal Total 28.37 8.65 17.11 1.87 0.80 0.54 0.40 Average 5.67 1.73 4.28 0.62 0.80 0.54 0.40 Std. Dev. 7.38 1.24 2.08 0.14 N/A N/A N/A

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Table 30. Addis Ababa Slaughter Plant Maximum Within-Species Contacts per Period.

Period Dog Cat Spotted Mongoose Black- African Honey Hyena backed Golden Badger Jackal Wolf 1 0 0 6 0 N/A 0 N/A 2 1 0 1 0 N/A 0 N/A 3 6 0 21 0 N/A 0 N/A 4 3 0 0 0 N/A 0 N/A 5 1 0 3 0 N/A 0 N/A 6 6 0 6 0 N/A 0 N/A 7 6 0 6 0 N/A 0 N/A 8 6 0 0 0 N/A 0 N/A 9 15 0 36 1 N/A 3 N/A 10 15 3 55 0 N/A 0 N/A 11 3 0 6 0 N/A 0 N/A 12 10 0 3 0 N/A 0 N/A 13 3 0 10 0 N/A 0 N/A Total 75.00 3.00 153.00 1.00 N/A 3 N/A Average 5.77 0.23 11.77 0.08 N/A 0.23 N/A St. Dev. 4.92 0.83 16.39 0.28 N/A 0.83 N/A

*N/A- species not present

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Table 31. Goba Slaughter Plant Maximum Within-Species Contacts per Period.

Period Dog Cat Spotted Mongoose Black- African Honey Hyena backed Golden Badger Jackal Wolf 1 253 0 N/A 0 N/A N/A N/A 2 55 0 N/A 0 N/A N/A N/A 3 528 0 N/A 0 N/A N/A N/A 4 28 0 N/A 0 N/A N/A N/A Total 864 0 N/A 0 N/A N/A N/A Average 216 0 N/A 0 N/A N/A N/A St. Dev. 230.92 0 N/A 0 N/A N/A N/A

*N/A- species not present

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Table 32. Awash Slaughter Plant Maximum Within-Species Contacts per Period.

Period Dog Cat Spotted Mongoose Black- African Honey Hyena backed Golden Badger Jackal Wolf 1 0 1 1 0 0 N/A 1 2 3 3 0 0 0 N/A 0 3 1 3 0 0 0 N/A 0 4 3 6 1 0 0 N/A 0 5 0 0 0 N/A 1 0 0 Total 7 14 2 0 0 N/A 1 Average 1.40 2.80 0.40 0 0 N/A 0.20 St. Dev. 1.52 2.05 0.55 0 0 N/A 0.45

*N/A- species not present

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Table 33. Awassa Slaughter Plant Maximum Within-Species Contacts per Period.

Period Dog Cat Spotted Mongoose Black- African Honey Hyena backed Golden Badger Jackal Wolf 1 1 3 45 N/A N/A N/A N/A 2 3 6 10 N/A N/A N/A N/A 3 6 3 6 N/A N/A N/A N/A Total 10 12 61 N/A N/A N/A N/A Average 3.33 4.00 20.33 N/A N/A N/A N/A St. Dev. 2.52 1.73 21.46 N/A N/A N/A N/A

*N/A- species not present

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Table 34. Awassa Waste Disposal Facility Maximum Within-Species Contacts per Period.

Period Dog Cat Spotted Mongoos Black-backed African Honey Hyena e Jackal Golden Wolf Badger 1 0 0 6 N/A N/A N/A N/A 2 0 0 6 N/A N/A N/A N/A 3 1 0 21 N/A N/A N/A N/A Total 1.00 0 33.00 N/A N/A N/A N/A Average 0.33 0 11.00 N/A N/A N/A N/A St. Dev. 0.58 0 8.66 N/A N/A N/A N/A

*N/A- species not present

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Table 35. Addis Ababa Slaughter Plant Maximum Between-Species Contacts per Period.

Period Dog-Cat Cat- Cat- Cat- Spotted Spotted Spotted Mongoose- Spotted Mongoose Black- Hyena- Hyena- Hyena- African Hyena backed Mongoose Black- African Golden Jackal backed Golden Wolf Jackal Wolf 1 0 0 0 N/A 2 N/A 0 0 2 0 0 0 N/A 0 N/A 0 0 3 0 0 0 N/A 0 N/A 0 0 4 0 0 0 N/A 0 N/A 0 0 5 0 0 0 N/A 0 N/A 0 0 6 0 0 0 N/A 0 N/A 0 0 7 0 0 0 N/A 0 N/A 0 0 8 0 0 0 N/A 0 N/A 0 0 9 0 0 0 N/A 4 N/A 0 0 10 0 4 0 N/A 2 N/A 0 0 11 0 0 0 N/A 0 N/A 0 0 12 0 0 0 N/A 0 N/A 0 0 13 0 0 0 N/A 1 N/A 1 1 Total 0 4 0 N/A 9 N/A 1 1 Average 0 0.31 0 N/A 0.69 N/A 0.08 0.08 St. Dev. 0 1.11 0 N/A 1.25 N/A 0.28 0.28

*N/A- One species not present

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Table 36. Goba Slaughter Plant Maximum Between-Species Contacts per Period.

Period Dog-Cat Cat- Cat- Cat- Spotted Spotted Spotted Mongoose- Spotted Mongoose Black- Hyena- Hyena- Hyena- African Hyena backed Mongoose Black- African Golden Jackal backed Golden Wolf Jackal Wolf 1 0 N/A 1 N/A N/A N/A N/A N/A 2 0 N/A 0 N/A N/A N/A N/A N/A 3 0 N/A 0 N/A N/A N/A N/A N/A 4 0 N/A 0 N/A N/A N/A N/A N/A Total 0 N/A 1 N/A N/A N/A N/A N/A Average 0 N/A 0.25 N/A N/A N/A N/A N/A St. Dev. 0 N/A 0.50 N/A N/A N/A N/A N/A

*N/A- One species not present

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Table 37. Awash Slaughter Plant Maximum Between-Species Contacts per Period.

Period Dog- Cat- Cat- Cat- Spotted Spotted Spotted Mongoose- Cat Spotted Mongoose Black- Hyena- Hyena- Hyena- African Hyena backed Mongoose Black- African Golden Jackal backed Golden Wolf Jackal Wolf 1 1 1 0 0 0 1 N/A N/A 2 2 1 1 1 0 1 N/A N/A 3 4 1 0 0 0 1 N/A N/A 4 3 1 0 0 0 0 N/A N/A 5 0 1 0 0 0 0 N/A N/A

Total 10 5 1 1 0 3 N/A N/A Average 2.00 1.00 0.20 0.20 0 0.60 N/A N/A St. Dev. 1.58 0.00 0.45 0.45 0 0.55 N/A N/A

*N/A- One species not present

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Table 38. Awassa Slaughter Plant Maximum Between-Species Contacts per Period.

Period Dog-Cat Cat- Cat- Cat- Spotted Spotted Spotted Mongoose- Spotted Mongoose Black- Hyena- Hyena- Hyena- African

Hyena backed Mongoose Black- African Golden Jackal backed Golden Wolf

Jackal Wolf 1 0 21 N/A N/A N/A N/A N/A N/A

2 0 15 N/A N/A N/A N/A N/A N/A 3 0 12 N/A N/A N/A N/A N/A N/A

Total 0 48 N/A N/A N/A N/A N/A N/A Average 0 16.00 N/A N/A N/A N/A N/A N/A

St. Dev. 0 4.58 N/A N/A N/A N/A N/A N/A

*N/A- One species not present

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Table 39. Awassa Waste Disposal Facility Maximum Between-Species Contacts per Period.

Period Dog-Cat Cat- Cat- Cat- Spotted Spotted Spotted Mongoose- Spotted Mongoose Black- Hyena- Hyena- Hyena- African Hyena backed Mongoose Black- African Golden Jackal backed Golden Wolf Jackal Wolf 1 0 0 N/A N/A N/A N/A N/A N/A 2 0 1 N/A N/A N/A N/A N/A N/A 3 0 5 N/A N/A N/A N/A N/A N/A Total 0 6 N/A N/A N/A N/A N/A N/A Average 0 2.00 N/A N/A N/A N/A N/A N/A St. Dev. 0 2.65 N/A N/A N/A N/A N/A N/A

*N/A- One species not present

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Figure 18. Species Rank Abundance Curves Using Averages of Maximum Individuals. Spotted Hyena is represented by a triangle (▲), domestic dog by a square (■), domestic cat by a circle (●), mongoose by a diamond (♦), blacked-backed jackal by a hollow upside-down triangle (˅), honey badger by a star (*) and African golden wolf by a cross (X).

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4 Maximum Maximum Individuals

0 12:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 6:00 7:00 8:00 9:00 10:00 11:00 AM AM AM AM AM AM AM AM AM AM AM PM PM PM PM PM PM Hours

Dog Cat African Golden Wolf Spotted Hyena Mongoose

Figure 19. Addis Ababa Maximum Number of Active Individuals per Hour Over Recording Period by Species.

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Goba 15

0 12:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 6:00 7:00 8:00 9:00 10:00 11:00 AM AM AM AM AM AM AM AM AM AM AM PM PM PM PM PM PM Hours

Dog Cat Mongoose

Figure 20. Goba Maximum Number of Active Individuals per Hour Over Recording Period by Species.

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4.5

3.5

2.5

1.5

Maximum Maximum Individuals 1

0.5

0 12:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 6:00 7:00 8:00 9:00 10:00 11:00 AM AM AM AM AM AM AM AM AM AM AM PM PM PM PM PM PM Hours

Dog Cat Black-backed Jackal Spotted Hyena Mongoose Honey Badgar

Figure 21. Awash Maximum Number of Active Individuals per Hour Over Recording Period by Species.

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4 Maximum Maximum Individuals

0 12:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 6:00 7:00 8:00 9:00 10:00 11:00 AM AM AM AM AM AM AM AM AM AM AM PM PM PM PM PM PM Hours

Dog Cat Spotted Hyena

Figure 22. Awassa Slaughter Plant Maximum Number of Active Individuals per Hour Over Recording Period by Species.

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Maximum Maximum Individuals 2

0 12:00 1:00 AM 2:00 AM 3:00 AM 4:00 AM 5:00 AM 6:00 AM 6:00 PM 7:00 PM 8:00 PM 9:00 PM 10:00 11:00 AM PM PM Hours

Dog Cat Spotted Hyena

Figure 23. Awassa Waste Disposal Facility Maximum Number of Active Individuals per Hour Over Recording Period by

Species

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Appendix B. R0 Calculations (Chapter 5)

Modeling rabies transmission within species

We represented rabies transmission using the susceptible, exposed, infectious, removed (SEIR) model for transmission of infectious diseases (Anderson & May 1991;

Keeling & Rohani 2008). Susceptible (S) individuals are those who never experienced rabies infection. Exposed (E) individuals had contact with an infectious individual and contracted rabies but are not yet capable of infecting other animals. Infectious (I) individuals had contracted rabies virus and are capable of infecting other animals given adequate contact. Removed (R) animals are those who die due to rabies infection after the infectious period. Following Begon et al., we represent transmission as a function of the contact rate between individual animals (푐), the probability (푝) that contact is with an infectious host, and the probability (휈) that contact between the susceptible and infectious host actually causes transmission of the virus to the susceptible animal (Begon et al.

2002). Let 퐸푡 represent the number of new rabies infections that occur in timestep 푡, such that

퐸푡 = 푐푝휈푆푡−1 . (1)

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In order to determine if each species could maintain endemic rabies entirely through within species contacts, we needed to quantify 푅0, which is the number of secondary cases produced by each infectious individual in a totally susceptible population. When 푅0 ≥ 1, the number of secondary cases greater than or equal to the number of primary cases, and transmission is maintained. When 푅0 < 1, the number of secondary cases is less than the number of primary cases, and transmission is not maintained. There are two steps to quantifying 푅0: first, we must derive a mathematical expression for the term using our parameterization of the SEIR model; second, we must parameterize the expression for 푅0 using the contacts determined from the camera trap data and disease parameters determined from the literature.

We derived a mathematical expression for 푅0 using the next-generation method

(Diekmann, Heesterbeek & Metz 1990; van den Driessche & Watmough 2002;

Heffernan, Smith & Wahl 2005). We used the SEIR equations parametrized with 푐, 푝, and 휈 (Begon et al. 2002), and determined that

푐휈 푅 = . (2) 0 훾

For the full set of equations and the step-by-step derivation, please see Appendix 1 in the

Supplementary Material. In order to determine if each species could maintain rabies, we assume that 푅0 = 1; then, solving equation 2 for 푐 would give the expression for the

327

minimum within species contact rate required for rabies maintenance, which we designate as 푐′. It follows that

푐′ > 훾/휈 . (3)

We consider that each of the five species might have different values for 푐′, 훾, and 휈;

′ therefore, we consider species-specific parameter values represented by 푐푖,푖, 훾푖, and 휈푖,푖, where 푖 = 1, … , 5. Then,

′ 푐푖,푖 > 훾푖/휈푖,푖 (4)

and equation 4 can be parameterized according to Table 1 to find the species-specific

′ minimum within species contact rate required for rabies maintenance 푐푖,푖.

To determine if each of the five species could maintain rabies with only within species

′ ′ contacts, we determined the relationship between 푐푖,푖 and 푞푖,푖. If 푐푖,푖 < 푞푖,푖, then species 푖

′ cannot maintain rabies with only within species contacts; if 푐푖,푖 ≥ 푞푖,푖, then species 푖 could maintain rabies with only within species contacts.

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Determining if pairs of wildlife species could comprise a reservoir for rabies maintenance

푐푖,푖 as the within species contact rate; now, let 푐푖,푗 represent the rate of contact between species 푖 and species 푗. Likewise, let 휈푖,푗 represent the probability of a bite when there is between species contact. Let 퐸푖,푡 represent number of new rabies infections that occur in species 푖 at timestep 푡. Assuming that a pair of species might maintain rabies via within and between species transmission, it follows that

퐸푖,푡 = (푐푖,푖푝푖휈푖,푖 + 푐푖,푗 푝푗휈푖,푗) 푆푖,푡−1. (5)

We derived a mathematical expression for 푅0 for the case of two species and assume that transmission can occur via within and between species contacts using the next-generation method (Diekmann, Heesterbeek & Metz 1990; van den Driessche & Watmough 2002;

Heffernan, Smith & Wahl 2005). We used the SEIR equations parametrized with 푐푖,푖, 푐푖,푗,

푝푖, 푝푗, 휈푖,푖 and 푣푖,푗 (Begon et al. 2002) and determined that

푐 휈 푐 휈 푐 휈 푐 휈 푐 휈 푐 휈 푅 = max ( 1,2 ⋅ 2,1 + 1,1 , 1,2 ⋅ 2,1 + 2,2 ) . (6) 0 훾 훾 훾 훾 훾 훾

329

For the full set of equations and the step-by-step derivation, please see Appendix 2 in the

Supplementary Material. In order to determine if each pair of species could maintain rabies, we calculate the 푅0 using equation 3. For each pair of species, we assume that they can maintain transmission if 푅0 ≥ 1; if 푅0 ≤ 1, then we assume that the pair of species cannot maintain rabies.

330